id sid tid token lemma pos cana-5313 1 1 communications communication NOUN cana-5313 1 2 on on ADP cana-5313 1 3 applied apply VERB cana-5313 1 4 nonlinear nonlinear ADJ cana-5313 1 5 analysis analysis NOUN cana-5313 1 6 issn issn NOUN cana-5313 1 7 : : PUNCT cana-5313 1 8 1074 1074 NUM cana-5313 1 9 - - PUNCT cana-5313 1 10 133x 133x NUM cana-5313 1 11 vol vol NOUN cana-5313 1 12 31 31 NUM cana-5313 1 13 no no NOUN cana-5313 1 14 . . PUNCT cana-5313 2 1 8s 8s PROPN cana-5313 2 2 ( ( PUNCT cana-5313 2 3 2024 2024 NUM cana-5313 2 4 ) ) PUNCT cana-5313 2 5 935 935 NUM cana-5313 2 6 https://internationalpubls.com https://internationalpubls.com X cana-5313 2 7 statistical statistical ADJ cana-5313 2 8 inference inference NOUN cana-5313 2 9 in in ADP cana-5313 2 10 machine machine NOUN cana-5313 2 11 learning learn VERB cana-5313 2 12 bridging bridge VERB cana-5313 2 13 probability probability NOUN cana-5313 2 14 and and CCONJ cana-5313 2 15 data datum NOUN cana-5313 2 16 science science NOUN cana-5313 2 17 1s 1s X cana-5313 2 18 . . PUNCT cana-5313 3 1 balamuralitharan balamuralitharan PROPN cana-5313 3 2 , , PUNCT cana-5313 3 3 2jenifer 2jenifer NUM cana-5313 3 4 ebienazer ebienazer NOUN cana-5313 3 5 j j NOUN cana-5313 3 6 , , PUNCT cana-5313 3 7 3r 3r NUM cana-5313 3 8 . . PUNCT cana-5313 4 1 arulprakasam arulprakasam PROPN cana-5313 4 2 , , PUNCT cana-5313 4 3 4d 4d NUM cana-5313 4 4 v v NUM cana-5313 4 5 l l NOUN cana-5313 4 6 prasanna prasanna PROPN cana-5313 4 7 , , PUNCT cana-5313 4 8 5b.krishnaveni 5b.krishnaveni NUM cana-5313 4 9 , , PUNCT cana-5313 4 10 adjunct adjunct ADJ cana-5313 4 11 faculty faculty NOUN cana-5313 4 12 , , PUNCT cana-5313 4 13 department department NOUN cana-5313 4 14 of of ADP cana-5313 4 15 pure pure ADJ cana-5313 4 16 and and CCONJ cana-5313 4 17 applied applied ADJ cana-5313 4 18 mathematics mathematic NOUN cana-5313 4 19 , , PUNCT cana-5313 4 20 saveetha saveetha PROPN cana-5313 4 21 school school PROPN cana-5313 4 22 of of ADP cana-5313 4 23 engineering engineering NOUN cana-5313 4 24 , , PUNCT cana-5313 4 25 simats simat NOUN cana-5313 4 26 , , PUNCT cana-5313 4 27 chennai chennai PROPN cana-5313 4 28 , , PUNCT cana-5313 4 29 tamil tamil PROPN cana-5313 4 30 nadu nadu PROPN cana-5313 4 31 , , PUNCT cana-5313 4 32 india india PROPN cana-5313 4 33 email email NOUN cana-5313 4 34 i i PROPN cana-5313 4 35 d d PROPN cana-5313 4 36 : : PUNCT cana-5313 4 37 balamurali.maths@gmail.com balamurali.maths@gmail.com X cana-5313 4 38 assistant assistant NOUN cana-5313 4 39 professor professor NOUN cana-5313 4 40 , , PUNCT cana-5313 4 41 sri sri PROPN cana-5313 4 42 sairam sairam PROPN cana-5313 4 43 college college PROPN cana-5313 4 44 of of ADP cana-5313 4 45 engineering engineering PROPN cana-5313 4 46 , , PUNCT cana-5313 4 47 bengaluru bengaluru PROPN cana-5313 4 48 jni5.nazr@gmail.com jni5.nazr@gmail.com PROPN cana-5313 4 49 department department PROPN cana-5313 4 50 of of ADP cana-5313 4 51 mathematics mathematics PROPN cana-5313 4 52 , , PUNCT cana-5313 4 53 college college NOUN cana-5313 4 54 of of ADP cana-5313 4 55 engineering engineering NOUN cana-5313 4 56 and and CCONJ cana-5313 4 57 technology technology NOUN cana-5313 4 58 , , PUNCT cana-5313 4 59 srm srm PROPN cana-5313 4 60 institute institute PROPN cana-5313 4 61 of of ADP cana-5313 4 62 science science NOUN cana-5313 4 63 and and CCONJ cana-5313 4 64 technology technology NOUN cana-5313 4 65 , , PUNCT cana-5313 4 66 srm srm NOUN cana-5313 4 67 nagar nagar NOUN cana-5313 4 68 , , PUNCT cana-5313 4 69 kattankulathur kattankulathur PROPN cana-5313 4 70 603203 603203 NUM cana-5313 4 71 , , PUNCT cana-5313 4 72 chengalpattu chengalpattu ADJ cana-5313 4 73 district district NOUN cana-5313 4 74 , , PUNCT cana-5313 4 75 tamilnadu tamilnadu PROPN cana-5313 4 76 , , PUNCT cana-5313 4 77 india india PROPN cana-5313 4 78 r.aruljeeva@gmail.com r.aruljeeva@gmail.com PROPN cana-5313 4 79 associate associate PROPN cana-5313 4 80 professor professor NOUN cana-5313 4 81 , , PUNCT cana-5313 4 82 department department NOUN cana-5313 4 83 of of ADP cana-5313 4 84 mathematics mathematics PROPN cana-5313 4 85 , , PUNCT cana-5313 4 86 aditya aditya PROPN cana-5313 4 87 university university PROPN cana-5313 4 88 , , PUNCT cana-5313 4 89 surampalem surampalem NOUN cana-5313 4 90 , , PUNCT cana-5313 4 91 india india PROPN cana-5313 4 92 , , PUNCT cana-5313 4 93 dvl.prasanna@aec.edu.in dvl.prasanna@aec.edu.in NOUN cana-5313 4 94 associate associate NOUN cana-5313 4 95 professor professor NOUN cana-5313 4 96 , , PUNCT cana-5313 4 97 dept dept NOUN cana-5313 4 98 of of ADP cana-5313 4 99 mathematics mathematic NOUN cana-5313 4 100 , , PUNCT cana-5313 4 101 aditya aditya PROPN cana-5313 4 102 university university PROPN cana-5313 4 103 , , PUNCT cana-5313 4 104 surampalem surampalem NOUN cana-5313 4 105 , , PUNCT cana-5313 4 106 india india PROPN cana-5313 4 107 , , PUNCT cana-5313 4 108 krishnaveni.b@aec.edu.in krishnaveni.b@aec.edu.in SYM cana-5313 4 109 article article NOUN cana-5313 4 110 history history NOUN cana-5313 4 111 : : PUNCT cana-5313 4 112 received receive VERB cana-5313 4 113 : : PUNCT cana-5313 4 114 12 12 NUM cana-5313 4 115 - - SYM cana-5313 4 116 10 10 NUM cana-5313 4 117 - - PUNCT cana-5313 4 118 2024 2024 NUM cana-5313 4 119 revised revise VERB cana-5313 4 120 : : PUNCT cana-5313 4 121 15 15 NUM cana-5313 4 122 - - SYM cana-5313 4 123 11 11 NUM cana-5313 4 124 - - PUNCT cana-5313 4 125 2024 2024 NUM cana-5313 4 126 accepted accept VERB cana-5313 4 127 : : PUNCT cana-5313 4 128 19 19 NUM cana-5313 4 129 - - SYM cana-5313 4 130 12 12 NUM cana-5313 4 131 - - PUNCT cana-5313 4 132 2024 2024 NUM cana-5313 4 133 abstract abstract NOUN cana-5313 4 134 : : PUNCT cana-5313 4 135 model model NOUN cana-5313 4 136 parameters parameter NOUN cana-5313 4 137 emerge emerge VERB cana-5313 4 138 from from ADP cana-5313 4 139 the the DET cana-5313 4 140 process process NOUN cana-5313 4 141 while while SCONJ cana-5313 4 142 uncertainty uncertainty NOUN cana-5313 4 143 measurement measurement NOUN cana-5313 4 144 and and CCONJ cana-5313 4 145 hypothesis hypothesis NOUN cana-5313 4 146 tests test NOUN cana-5313 4 147 are be AUX cana-5313 4 148 its its PRON cana-5313 4 149 essential essential ADJ cana-5313 4 150 outputs output NOUN cana-5313 4 151 which which PRON cana-5313 4 152 machine machine NOUN cana-5313 4 153 learning learn VERB cana-5313 4 154 algorithms algorithm NOUN cana-5313 4 155 use use VERB cana-5313 4 156 to to PART cana-5313 4 157 draw draw VERB cana-5313 4 158 meaningful meaningful ADJ cana-5313 4 159 data datum NOUN cana-5313 4 160 conclusions conclusion NOUN cana-5313 4 161 . . PUNCT cana-5313 5 1 the the DET cana-5313 5 2 paper paper NOUN cana-5313 5 3 explores explore VERB cana-5313 5 4 statistical statistical ADJ cana-5313 5 5 inference inference NOUN cana-5313 5 6 role role NOUN cana-5313 5 7 in in ADP cana-5313 5 8 machine machine NOUN cana-5313 5 9 learning learning NOUN cana-5313 5 10 while while SCONJ cana-5313 5 11 examining examine VERB cana-5313 5 12 inference inference NOUN cana-5313 5 13 methods method NOUN cana-5313 5 14 between between ADP cana-5313 5 15 probability probability NOUN cana-5313 5 16 theory theory NOUN cana-5313 5 17 and and CCONJ cana-5313 5 18 machine machine NOUN cana-5313 5 19 learning learn VERB cana-5313 5 20 along along ADP cana-5313 5 21 with with ADP cana-5313 5 22 a a DET cana-5313 5 23 detailed detailed ADJ cana-5313 5 24 approach approach NOUN cana-5313 5 25 for for ADP cana-5313 5 26 connecting connect VERB cana-5313 5 27 these these DET cana-5313 5 28 domains domain NOUN cana-5313 5 29 . . PUNCT cana-5313 6 1 the the DET cana-5313 6 2 paper paper NOUN cana-5313 6 3 examines examine VERB cana-5313 6 4 maximum maximum ADJ cana-5313 6 5 likelihood likelihood NOUN cana-5313 6 6 estimation estimation NOUN cana-5313 6 7 ( ( PUNCT cana-5313 6 8 mle mle PROPN cana-5313 6 9 ) ) PUNCT cana-5313 6 10 along along ADP cana-5313 6 11 with with ADP cana-5313 6 12 bayesian bayesian NOUN cana-5313 6 13 inference inference NOUN cana-5313 6 14 and and CCONJ cana-5313 6 15 frequentist frequentist NOUN cana-5313 6 16 techniques technique NOUN cana-5313 6 17 as as ADP cana-5313 6 18 main main ADJ cana-5313 6 19 approaches approach NOUN cana-5313 6 20 in in ADP cana-5313 6 21 statistical statistical ADJ cana-5313 6 22 inference inference NOUN cana-5313 6 23 . . PUNCT cana-5313 7 1 the the DET cana-5313 7 2 paper paper NOUN cana-5313 7 3 demonstrates demonstrate VERB cana-5313 7 4 ways way NOUN cana-5313 7 5 to to PART cana-5313 7 6 use use VERB cana-5313 7 7 these these DET cana-5313 7 8 methods method NOUN cana-5313 7 9 in in ADP cana-5313 7 10 combination combination NOUN cana-5313 7 11 with with ADP cana-5313 7 12 standard standard ADJ cana-5313 7 13 machine machine NOUN cana-5313 7 14 learning learn VERB cana-5313 7 15 algorithms algorithm NOUN cana-5313 7 16 because because SCONJ cana-5313 7 17 it it PRON cana-5313 7 18 improves improve VERB cana-5313 7 19 both both DET cana-5313 7 20 model model NOUN cana-5313 7 21 performance performance NOUN cana-5313 7 22 and and CCONJ cana-5313 7 23 decision decision NOUN cana-5313 7 24 systems system NOUN cana-5313 7 25 . . PUNCT cana-5313 8 1 the the DET cana-5313 8 2 paper paper NOUN cana-5313 8 3 introduces introduce VERB cana-5313 8 4 an an DET cana-5313 8 5 extensive extensive ADJ cana-5313 8 6 methodology methodology NOUN cana-5313 8 7 that that PRON cana-5313 8 8 uses use VERB cana-5313 8 9 statistical statistical ADJ cana-5313 8 10 inference inference NOUN cana-5313 8 11 approaches approach NOUN cana-5313 8 12 in in ADP cana-5313 8 13 machine machine NOUN cana-5313 8 14 learning learning NOUN cana-5313 8 15 and and CCONJ cana-5313 8 16 presents present VERB cana-5313 8 17 mathematical mathematical ADJ cana-5313 8 18 statements statement NOUN cana-5313 8 19 together together ADV cana-5313 8 20 with with ADP cana-5313 8 21 their their PRON cana-5313 8 22 effects effect NOUN cana-5313 8 23 on on ADP cana-5313 8 24 predictive predictive ADJ cana-5313 8 25 and and CCONJ cana-5313 8 26 evaluating evaluating NOUN cana-5313 8 27 models model NOUN cana-5313 8 28 and and CCONJ cana-5313 8 29 generalization generalization NOUN cana-5313 8 30 . . PUNCT cana-5313 9 1 keywords keyword NOUN cana-5313 9 2 β€” β€” PUNCT cana-5313 9 3 statistical statistical ADJ cana-5313 9 4 inference inference NOUN cana-5313 9 5 , , PUNCT cana-5313 9 6 machine machine NOUN cana-5313 9 7 learning learning NOUN cana-5313 9 8 , , PUNCT cana-5313 9 9 probability probability NOUN cana-5313 9 10 theory theory NOUN cana-5313 9 11 , , PUNCT cana-5313 9 12 maximum maximum ADJ cana-5313 9 13 likelihood likelihood NOUN cana-5313 9 14 estimation estimation NOUN cana-5313 9 15 , , PUNCT cana-5313 9 16 bayesian bayesian NOUN cana-5313 9 17 inference inference NOUN cana-5313 9 18 , , PUNCT cana-5313 9 19 hypothesis hypothesis NOUN cana-5313 9 20 testing testing NOUN cana-5313 9 21 , , PUNCT cana-5313 9 22 data data NOUN cana-5313 9 23 science science NOUN cana-5313 9 24 , , PUNCT cana-5313 9 25 model model NOUN cana-5313 9 26 evaluation evaluation NOUN cana-5313 9 27 , , PUNCT cana-5313 9 28 uncertainty uncertainty NOUN cana-5313 9 29 quantification quantification NOUN cana-5313 9 30 i. i. NOUN cana-5313 9 31 introduction introduction NOUN cana-5313 9 32 machine machine NOUN cana-5313 9 33 learning learning NOUN cana-5313 9 34 gets get VERB cana-5313 9 35 its its PRON cana-5313 9 36 fundamental fundamental ADJ cana-5313 9 37 predictive predictive ADJ cana-5313 9 38 capabilities capability NOUN cana-5313 9 39 through through ADP cana-5313 9 40 statistical statistical ADJ cana-5313 9 41 inference inference NOUN cana-5313 9 42 by by ADP cana-5313 9 43 using use VERB cana-5313 9 44 probability probability NOUN cana-5313 9 45 theory theory NOUN cana-5313 9 46 to to PART cana-5313 9 47 analyze analyze VERB cana-5313 9 48 data datum NOUN cana-5313 9 49 in in ADP cana-5313 9 50 order order NOUN cana-5313 9 51 to to PART cana-5313 9 52 make make VERB cana-5313 9 53 reliable reliable ADJ cana-5313 9 54 results result NOUN cana-5313 9 55 . . PUNCT cana-5313 10 1 valid valid ADJ cana-5313 10 2 conclusions conclusion NOUN cana-5313 10 3 derived derive VERB cana-5313 10 4 from from ADP cana-5313 10 5 data datum NOUN cana-5313 10 6 create create VERB cana-5313 10 7 two two NUM cana-5313 10 8 beneficial beneficial ADJ cana-5313 10 9 effects effect NOUN cana-5313 10 10 that that PRON cana-5313 10 11 improve improve VERB cana-5313 10 12 model model NOUN cana-5313 10 13 functionality functionality NOUN cana-5313 10 14 and and CCONJ cana-5313 10 15 uncertainty uncertainty NOUN cana-5313 10 16 prediction prediction NOUN cana-5313 10 17 capability capability NOUN cana-5313 10 18 [ [ X cana-5313 10 19 1 1 NUM cana-5313 10 20 - - SYM cana-5313 10 21 2 2 NUM cana-5313 10 22 ] ] PUNCT cana-5313 10 23 . . PUNCT cana-5313 11 1 statistical statistical ADJ cana-5313 11 2 inference inference NOUN cana-5313 11 3 contains contain VERB cana-5313 11 4 three three NUM cana-5313 11 5 fundamental fundamental ADJ cana-5313 11 6 concepts concept NOUN cana-5313 11 7 of of ADP cana-5313 11 8 estimating estimate VERB cana-5313 11 9 model model NOUN cana-5313 11 10 parameters parameter NOUN cana-5313 11 11 combined combine VERB cana-5313 11 12 with with ADP cana-5313 11 13 uncertainty uncertainty NOUN cana-5313 11 14 quantification quantification NOUN cana-5313 11 15 and and CCONJ cana-5313 11 16 prediction prediction NOUN cana-5313 11 17 from from ADP cana-5313 11 18 the the DET cana-5313 11 19 provided provide VERB cana-5313 11 20 data datum NOUN cana-5313 11 21 . . PUNCT cana-5313 12 1 machine machine NOUN cana-5313 12 2 learning learning NOUN cana-5313 12 3 requires require VERB cana-5313 12 4 these these DET cana-5313 12 5 tasks task NOUN cana-5313 12 6 especially especially ADV cana-5313 12 7 because because SCONJ cana-5313 12 8 real real ADJ cana-5313 12 9 - - PUNCT cana-5313 12 10 world world NOUN cana-5313 12 11 data datum NOUN cana-5313 12 12 sets set NOUN cana-5313 12 13 come come VERB cana-5313 12 14 with with ADP cana-5313 12 15 noise noise NOUN cana-5313 12 16 while while SCONJ cana-5313 12 17 containing contain VERB cana-5313 12 18 incomplete incomplete ADJ cana-5313 12 19 records record NOUN cana-5313 12 20 as as ADV cana-5313 12 21 well well ADV cana-5313 12 22 as as ADP cana-5313 12 23 significant significant ADJ cana-5313 12 24 variations variation NOUN cana-5313 12 25 between between ADP cana-5313 12 26 samples sample NOUN cana-5313 12 27 . . PUNCT cana-5313 13 1 communications communication NOUN cana-5313 13 2 on on ADP cana-5313 13 3 applied apply VERB cana-5313 13 4 nonlinear nonlinear ADJ cana-5313 13 5 analysis analysis NOUN cana-5313 13 6 issn issn NOUN cana-5313 13 7 : : PUNCT cana-5313 13 8 1074 1074 NUM cana-5313 13 9 - - PUNCT cana-5313 13 10 133x 133x NUM cana-5313 13 11 vol vol NOUN cana-5313 13 12 31 31 NUM cana-5313 13 13 no no NOUN cana-5313 13 14 . . PUNCT cana-5313 14 1 8s 8s PROPN cana-5313 14 2 ( ( PUNCT cana-5313 14 3 2024 2024 NUM cana-5313 14 4 ) ) PUNCT cana-5313 14 5 936 936 NUM cana-5313 14 6 https://internationalpubls.com https://internationalpubls.com X cana-5313 14 7 mle mle PROPN cana-5313 14 8 functions function NOUN cana-5313 14 9 as as ADP cana-5313 14 10 a a DET cana-5313 14 11 commonly commonly ADV cana-5313 14 12 employed employ VERB cana-5313 14 13 method method NOUN cana-5313 14 14 to to PART cana-5313 14 15 calculate calculate VERB cana-5313 14 16 parameter parameter NOUN cana-5313 14 17 values value NOUN cana-5313 14 18 inside inside ADP cana-5313 14 19 probabilistic probabilistic ADJ cana-5313 14 20 models model NOUN cana-5313 14 21 . . PUNCT cana-5313 15 1 the the DET cana-5313 15 2 method method NOUN cana-5313 15 3 seeks seek VERB cana-5313 15 4 to to PART cana-5313 15 5 locate locate VERB cana-5313 15 6 parameter parameter NOUN cana-5313 15 7 settings setting NOUN cana-5313 15 8 in in ADP cana-5313 15 9 models model NOUN cana-5313 15 10 which which PRON cana-5313 15 11 would would AUX cana-5313 15 12 optimize optimize VERB cana-5313 15 13 the the DET cana-5313 15 14 possibility possibility NOUN cana-5313 15 15 of of ADP cana-5313 15 16 observing observe VERB cana-5313 15 17 the the DET cana-5313 15 18 existing exist VERB cana-5313 15 19 data data NOUN cana-5313 15 20 points point NOUN cana-5313 15 21 . . PUNCT cana-5313 16 1 the the DET cana-5313 16 2 learning learning NOUN cana-5313 16 3 process process NOUN cana-5313 16 4 within within ADP cana-5313 16 5 bayesian bayesian NOUN cana-5313 16 6 inference inference NOUN cana-5313 16 7 works work VERB cana-5313 16 8 by by ADP cana-5313 16 9 integrating integrate VERB cana-5313 16 10 existing exist VERB cana-5313 16 11 knowledge knowledge NOUN cana-5313 16 12 or or CCONJ cana-5313 16 13 beliefs belief NOUN cana-5313 16 14 of of ADP cana-5313 16 15 the the DET cana-5313 16 16 system system NOUN cana-5313 16 17 . . PUNCT cana-5313 17 1 these these DET cana-5313 17 2 methods method NOUN cana-5313 17 3 strengthen strengthen VERB cana-5313 17 4 model model NOUN cana-5313 17 5 analysis analysis NOUN cana-5313 17 6 specifically specifically ADV cana-5313 17 7 for for ADP cana-5313 17 8 cases case NOUN cana-5313 17 9 of of ADP cana-5313 17 10 scarce scarce ADJ cana-5313 17 11 data datum NOUN cana-5313 17 12 and and CCONJ cana-5313 17 13 high high ADJ cana-5313 17 14 levels level NOUN cana-5313 17 15 of of ADP cana-5313 17 16 uncertainty uncertainty NOUN cana-5313 17 17 through through ADP cana-5313 17 18 the the DET cana-5313 17 19 addition addition NOUN cana-5313 17 20 of of ADP cana-5313 17 21 updated update VERB cana-5313 17 22 belief belief NOUN cana-5313 17 23 systems system NOUN cana-5313 17 24 . . PUNCT cana-5313 18 1 the the DET cana-5313 18 2 field field NOUN cana-5313 18 3 of of ADP cana-5313 18 4 hypothesis hypothesis NOUN cana-5313 18 5 testing testing NOUN cana-5313 18 6 and and CCONJ cana-5313 18 7 parameter parameter NOUN cana-5313 18 8 estimation estimation NOUN cana-5313 18 9 through through ADP cana-5313 18 10 frequentist frequentist NOUN cana-5313 18 11 processes process NOUN cana-5313 18 12 benefits benefit NOUN cana-5313 18 13 from from ADP cana-5313 18 14 repeated repeat VERB cana-5313 18 15 sampling sampling NOUN cana-5313 18 16 math math NOUN cana-5313 18 17 since since SCONJ cana-5313 18 18 it it PRON cana-5313 18 19 eliminates eliminate VERB cana-5313 18 20 the the DET cana-5313 18 21 necessity necessity NOUN cana-5313 18 22 of of ADP cana-5313 18 23 prior prior ADJ cana-5313 18 24 distribution distribution NOUN cana-5313 18 25 specifications specification NOUN cana-5313 18 26 [ [ X cana-5313 18 27 10 10 NUM cana-5313 18 28 ] ] PUNCT cana-5313 18 29 . . PUNCT cana-5313 19 1 machine machine NOUN cana-5313 19 2 learning learn VERB cana-5313 19 3 frameworks framework NOUN cana-5313 19 4 benefit benefit VERB cana-5313 19 5 from from ADP cana-5313 19 6 mathematical mathematical ADJ cana-5313 19 7 integration integration NOUN cana-5313 19 8 of of ADP cana-5313 19 9 statistical statistical ADJ cana-5313 19 10 methods method NOUN cana-5313 19 11 to to PART cana-5313 19 12 enable enable VERB cana-5313 19 13 better well ADJ cana-5313 19 14 procedures procedure NOUN cana-5313 19 15 in in ADP cana-5313 19 16 model model NOUN cana-5313 19 17 development development NOUN cana-5313 19 18 . . PUNCT cana-5313 20 1 a a DET cana-5313 20 2 significant significant ADJ cana-5313 20 3 number number NOUN cana-5313 20 4 of of ADP cana-5313 20 5 practitioners practitioner NOUN cana-5313 20 6 tend tend VERB cana-5313 20 7 to to PART cana-5313 20 8 disregard disregard VERB cana-5313 20 9 the the DET cana-5313 20 10 importance importance NOUN cana-5313 20 11 of of ADP cana-5313 20 12 statistical statistical ADJ cana-5313 20 13 inference inference NOUN cana-5313 20 14 during during ADP cana-5313 20 15 machine machine NOUN cana-5313 20 16 learning learning NOUN cana-5313 20 17 model model NOUN cana-5313 20 18 development development NOUN cana-5313 20 19 processes process NOUN cana-5313 20 20 . . PUNCT cana-5313 21 1 many many ADJ cana-5313 21 2 practitioners practitioner NOUN cana-5313 21 3 focus focus VERB cana-5313 21 4 on on ADP cana-5313 21 5 achieving achieve VERB cana-5313 21 6 high high ADJ cana-5313 21 7 performance performance NOUN cana-5313 21 8 metrics metric NOUN cana-5313 21 9 instead instead ADV cana-5313 21 10 of of ADP cana-5313 21 11 using use VERB cana-5313 21 12 statistical statistical ADJ cana-5313 21 13 principles principle NOUN cana-5313 21 14 because because SCONJ cana-5313 21 15 they they PRON cana-5313 21 16 omit omit VERB cana-5313 21 17 the the DET cana-5313 21 18 foundation foundation NOUN cana-5313 21 19 for for ADP cana-5313 21 20 stable stable ADJ cana-5313 21 21 and and CCONJ cana-5313 21 22 readable readable ADJ cana-5313 21 23 outcomes outcome NOUN cana-5313 21 24 [ [ X cana-5313 21 25 11 11 NUM cana-5313 21 26 ] ] PUNCT cana-5313 21 27 . . PUNCT cana-5313 22 1 novelty novelty NOUN cana-5313 22 2 and and CCONJ cana-5313 22 3 contribution contribution NOUN cana-5313 22 4 this this PRON cana-5313 22 5 is be AUX cana-5313 22 6 the the DET cana-5313 22 7 novelty novelty NOUN cana-5313 22 8 of of ADP cana-5313 22 9 the the DET cana-5313 22 10 paper paper NOUN cana-5313 22 11 : : PUNCT cana-5313 22 12 to to PART cana-5313 22 13 bridge bridge VERB cana-5313 22 14 the the DET cana-5313 22 15 gap gap NOUN cana-5313 22 16 between between ADP cana-5313 22 17 statistical statistical ADJ cana-5313 22 18 inference inference NOUN cana-5313 22 19 and and CCONJ cana-5313 22 20 machine machine NOUN cana-5313 22 21 learning learn VERB cana-5313 22 22 by by ADP cana-5313 22 23 studying study VERB cana-5313 22 24 in in ADP cana-5313 22 25 detail detail NOUN cana-5313 22 26 how how SCONJ cana-5313 22 27 the the DET cana-5313 22 28 traditional traditional ADJ cana-5313 22 29 statistical statistical ADJ cana-5313 22 30 techniques technique NOUN cana-5313 22 31 can can AUX cana-5313 22 32 improve improve VERB cana-5313 22 33 interpretability interpretability NOUN cana-5313 22 34 , , PUNCT cana-5313 22 35 reliability reliability NOUN cana-5313 22 36 , , PUNCT cana-5313 22 37 and and CCONJ cana-5313 22 38 generalization generalization NOUN cana-5313 22 39 of of ADP cana-5313 22 40 the the DET cana-5313 22 41 machine machine NOUN cana-5313 22 42 learning learning NOUN cana-5313 22 43 models model NOUN cana-5313 22 44 . . PUNCT cana-5313 23 1 while while SCONJ cana-5313 23 2 statistical statistical ADJ cana-5313 23 3 method method NOUN cana-5313 23 4 such such ADJ cana-5313 23 5 as as ADP cana-5313 23 6 maximum maximum ADJ cana-5313 23 7 likelihood likelihood NOUN cana-5313 23 8 estimation estimation NOUN cana-5313 23 9 ( ( PUNCT cana-5313 23 10 mle mle PROPN cana-5313 23 11 ) ) PUNCT cana-5313 23 12 , , PUNCT cana-5313 23 13 bayesian bayesian NOUN cana-5313 23 14 inference inference NOUN cana-5313 23 15 , , PUNCT cana-5313 23 16 frequentist frequentist NOUN cana-5313 23 17 approaches approach NOUN cana-5313 23 18 have have AUX cana-5313 23 19 been be AUX cana-5313 23 20 used use VERB cana-5313 23 21 to to ADP cana-5313 23 22 machine machine NOUN cana-5313 23 23 learning learning NOUN cana-5313 23 24 in in ADP cana-5313 23 25 various various ADJ cana-5313 23 26 manners manner NOUN cana-5313 23 27 , , PUNCT cana-5313 23 28 this this DET cana-5313 23 29 paper paper NOUN cana-5313 23 30 links link VERB cana-5313 23 31 them they PRON cana-5313 23 32 into into ADP cana-5313 23 33 a a DET cana-5313 23 34 unified unified ADJ cana-5313 23 35 framework framework NOUN cana-5313 23 36 . . PUNCT cana-5313 24 1 these these DET cana-5313 24 2 two two NUM cana-5313 24 3 methods method NOUN cana-5313 24 4 are be AUX cana-5313 24 5 combined combine VERB cana-5313 24 6 for for ADP cana-5313 24 7 an an DET cana-5313 24 8 integration integration NOUN cana-5313 24 9 that that PRON cana-5313 24 10 shows show VERB cana-5313 24 11 how how SCONJ cana-5313 24 12 they they PRON cana-5313 24 13 can can AUX cana-5313 24 14 be be AUX cana-5313 24 15 used use VERB cana-5313 24 16 to to ADP cana-5313 24 17 different different ADJ cana-5313 24 18 aspects aspect NOUN cana-5313 24 19 of of ADP cana-5313 24 20 model model NOUN cana-5313 24 21 development development NOUN cana-5313 24 22 including include VERB cana-5313 24 23 parameter parameter NOUN cana-5313 24 24 estimation estimation NOUN cana-5313 24 25 and and CCONJ cana-5313 24 26 uncertainty uncertainty NOUN cana-5313 24 27 quantification quantification NOUN cana-5313 24 28 as as ADV cana-5313 24 29 well well ADV cana-5313 24 30 as as ADP cana-5313 24 31 model model NOUN cana-5313 24 32 testing testing NOUN cana-5313 24 33 and and CCONJ cana-5313 24 34 validation validation NOUN cana-5313 24 35 [ [ X cana-5313 24 36 13 13 NUM cana-5313 24 37 - - SYM cana-5313 24 38 15 15 NUM cana-5313 24 39 ] ] PUNCT cana-5313 24 40 . . PUNCT cana-5313 25 1 in in ADP cana-5313 25 2 addition addition NOUN cana-5313 25 3 , , PUNCT cana-5313 25 4 this this DET cana-5313 25 5 paper paper NOUN cana-5313 25 6 also also ADV cana-5313 25 7 contributes contribute VERB cana-5313 25 8 to to ADP cana-5313 25 9 the the DET cana-5313 25 10 body body NOUN cana-5313 25 11 of of ADP cana-5313 25 12 existing exist VERB cana-5313 25 13 literature literature NOUN cana-5313 25 14 by by ADP cana-5313 25 15 providing provide VERB cana-5313 25 16 mathematical mathematical ADJ cana-5313 25 17 formulations formulation NOUN cana-5313 25 18 of of ADP cana-5313 25 19 these these DET cana-5313 25 20 statistical statistical ADJ cana-5313 25 21 methods method NOUN cana-5313 25 22 in in ADP cana-5313 25 23 terms term NOUN cana-5313 25 24 of of ADP cana-5313 25 25 details detail NOUN cana-5313 25 26 , , PUNCT cana-5313 25 27 and and CCONJ cana-5313 25 28 their their PRON cana-5313 25 29 use use NOUN cana-5313 25 30 within within ADP cana-5313 25 31 the the DET cana-5313 25 32 context context NOUN cana-5313 25 33 machine machine NOUN cana-5313 25 34 learning learning NOUN cana-5313 25 35 , , PUNCT cana-5313 25 36 thereby thereby ADV cana-5313 25 37 making make VERB cana-5313 25 38 it it PRON cana-5313 25 39 easier easy ADJ cana-5313 25 40 for for SCONJ cana-5313 25 41 practitioners practitioner NOUN cana-5313 25 42 to to PART cana-5313 25 43 understand understand VERB cana-5313 25 44 the the DET cana-5313 25 45 utilization utilization NOUN cana-5313 25 46 and and CCONJ cana-5313 25 47 practice practice NOUN cana-5313 25 48 of of ADP cana-5313 25 49 the the DET cana-5313 25 50 formulated formulate VERB cana-5313 25 51 statistical statistical ADJ cana-5313 25 52 methods method NOUN cana-5313 25 53 in in ADP cana-5313 25 54 real real ADJ cana-5313 25 55 world world NOUN cana-5313 25 56 scenarios scenario NOUN cana-5313 25 57 . . PUNCT cana-5313 26 1 this this DET cana-5313 26 2 work work NOUN cana-5313 26 3 conveys convey VERB cana-5313 26 4 the the DET cana-5313 26 5 importance importance NOUN cana-5313 26 6 of of ADP cana-5313 26 7 uncertainty uncertainty NOUN cana-5313 26 8 quantification quantification NOUN cana-5313 26 9 which which PRON cana-5313 26 10 pushes push VERB cana-5313 26 11 for for ADP cana-5313 26 12 less less ADJ cana-5313 26 13 black black ADJ cana-5313 26 14 - - PUNCT cana-5313 26 15 box box NOUN cana-5313 26 16 perspective perspective NOUN cana-5313 26 17 of of ADP cana-5313 26 18 evaluating evaluate VERB cana-5313 26 19 a a DET cana-5313 26 20 model model NOUN cana-5313 26 21 and and CCONJ cana-5313 26 22 a a DET cana-5313 26 23 more more ADV cana-5313 26 24 nuanced nuanced ADJ cana-5313 26 25 method method NOUN cana-5313 26 26 by by ADP cana-5313 26 27 adopting adopt VERB cana-5313 26 28 performance performance NOUN cana-5313 26 29 metrics metric NOUN cana-5313 26 30 along along ADV cana-5313 26 31 with with ADP cana-5313 26 32 uncertainty uncertainty NOUN cana-5313 26 33 measures measure NOUN cana-5313 26 34 to to PART cana-5313 26 35 display display VERB cana-5313 26 36 model model NOUN cana-5313 26 37 predictions prediction NOUN cana-5313 26 38 in in ADP cana-5313 26 39 a a DET cana-5313 26 40 robustnier robustnier NOUN cana-5313 26 41 way way NOUN cana-5313 26 42 . . PUNCT cana-5313 27 1 finally finally ADV cana-5313 27 2 , , PUNCT cana-5313 27 3 the the DET cana-5313 27 4 paper paper NOUN cana-5313 27 5 also also ADV cana-5313 27 6 considers consider VERB cana-5313 27 7 the the DET cana-5313 27 8 untouched untouched ADJ cana-5313 27 9 territory territory NOUN cana-5313 27 10 of of ADP cana-5313 27 11 hybrid hybrid ADJ cana-5313 27 12 inference inference NOUN cana-5313 27 13 models model NOUN cana-5313 27 14 , , PUNCT cana-5313 27 15 i.e. i.e. X cana-5313 27 16 frequentist frequentist NOUN cana-5313 27 17 and and CCONJ cana-5313 27 18 bayesian bayesian NOUN cana-5313 27 19 models model NOUN cana-5313 27 20 used use VERB cana-5313 27 21 together together ADV cana-5313 27 22 to to PART cana-5313 27 23 obtain obtain VERB cana-5313 27 24 more more ADV cana-5313 27 25 flexible flexible ADJ cana-5313 27 26 and and CCONJ cana-5313 27 27 scalable scalable ADJ cana-5313 27 28 models model NOUN cana-5313 27 29 . . PUNCT cana-5313 28 1 in in ADP cana-5313 28 2 contrast contrast NOUN cana-5313 28 3 , , PUNCT cana-5313 28 4 this this DET cana-5313 28 5 contribution contribution NOUN cana-5313 28 6 provides provide VERB cana-5313 28 7 a a DET cana-5313 28 8 new new ADJ cana-5313 28 9 view view NOUN cana-5313 28 10 on on ADP cana-5313 28 11 how how SCONJ cana-5313 28 12 different different ADJ cana-5313 28 13 statistical statistical ADJ cana-5313 28 14 paradigms paradigms NOUN cana-5313 28 15 can can AUX cana-5313 28 16 complement complement VERB cana-5313 28 17 each each DET cana-5313 28 18 other other ADJ cana-5313 28 19 to to PART cana-5313 28 20 deal deal VERB cana-5313 28 21 with with ADP cana-5313 28 22 different different ADJ cana-5313 28 23 types type NOUN cana-5313 28 24 of of ADP cana-5313 28 25 data datum NOUN cana-5313 28 26 and and CCONJ cana-5313 28 27 model model NOUN cana-5313 28 28 assumptions assumption NOUN cana-5313 28 29 and and CCONJ cana-5313 28 30 this this PRON cana-5313 28 31 leads lead VERB cana-5313 28 32 to to ADP cana-5313 28 33 enhanced enhanced ADJ cana-5313 28 34 solutions solution NOUN cana-5313 28 35 of of ADP cana-5313 28 36 complex complex ADJ cana-5313 28 37 machine machine NOUN cana-5313 28 38 learning learning NOUN cana-5313 28 39 problems problem NOUN cana-5313 28 40 . . PUNCT cana-5313 29 1 this this DET cana-5313 29 2 paper paper NOUN cana-5313 29 3 provides provide VERB cana-5313 29 4 practical practical ADJ cana-5313 29 5 use use NOUN cana-5313 29 6 in in ADP cana-5313 29 7 sciences science NOUN cana-5313 29 8 across across ADP cana-5313 29 9 healthcare healthcare PROPN cana-5313 29 10 alongside alongside ADP cana-5313 29 11 finance finance NOUN cana-5313 29 12 and and CCONJ cana-5313 29 13 autonomous autonomous ADJ cana-5313 29 14 systems system NOUN cana-5313 29 15 because because SCONJ cana-5313 29 16 model model ADJ cana-5313 29 17 uncertainty uncertainty NOUN cana-5313 29 18 and and CCONJ cana-5313 29 19 interpretability interpretability NOUN cana-5313 29 20 matters matter VERB cana-5313 29 21 in in ADP cana-5313 29 22 these these DET cana-5313 29 23 fields field NOUN cana-5313 29 24 . . PUNCT cana-5313 30 1 the the DET cana-5313 30 2 paper paper NOUN cana-5313 30 3 demonstrates demonstrate VERB cana-5313 30 4 how how SCONJ cana-5313 30 5 statistical statistical ADJ cana-5313 30 6 inference inference NOUN cana-5313 30 7 enables enable VERB cana-5313 30 8 trust trust NOUN cana-5313 30 9 in in ADP cana-5313 30 10 data data NOUN cana-5313 30 11 - - PUNCT cana-5313 30 12 driven drive VERB cana-5313 30 13 models model NOUN cana-5313 30 14 that that PRON cana-5313 30 15 are be AUX cana-5313 30 16 developed develop VERB cana-5313 30 17 for for ADP cana-5313 30 18 predictive predictive ADJ cana-5313 30 19 purposes purpose NOUN cana-5313 30 20 . . PUNCT cana-5313 31 1 the the DET cana-5313 31 2 paper paper NOUN cana-5313 31 3 helps help VERB cana-5313 31 4 establish establish VERB cana-5313 31 5 both both DET cana-5313 31 6 theoretical theoretical ADJ cana-5313 31 7 foundations foundation NOUN cana-5313 31 8 and and CCONJ cana-5313 31 9 practical practical ADJ cana-5313 31 10 deployment deployment NOUN cana-5313 31 11 methods method NOUN cana-5313 31 12 of of ADP cana-5313 31 13 statistical statistical ADJ cana-5313 31 14 inference inference NOUN cana-5313 31 15 in in ADP cana-5313 31 16 modern modern ADJ cana-5313 31 17 machine machine NOUN cana-5313 31 18 learning learn VERB cana-5313 31 19 techniques technique NOUN cana-5313 31 20 [ [ X cana-5313 31 21 9 9 NUM cana-5313 31 22 ] ] SYM cana-5313 31 23 . . PUNCT cana-5313 32 1 ii ii PROPN cana-5313 32 2 . . PROPN cana-5313 32 3 related relate VERB cana-5313 32 4 works work NOUN cana-5313 32 5 in in ADP cana-5313 32 6 2022 2022 NUM cana-5313 32 7 p. p. NOUN cana-5313 32 8 cui cui PROPN cana-5313 32 9 et.al et.al PROPN cana-5313 32 10 . . PUNCT cana-5313 32 11 and and CCONJ cana-5313 32 12 s. s. PROPN cana-5313 32 13 athey athey PROPN cana-5313 32 14 et.al et.al PROPN cana-5313 32 15 . . PUNCT cana-5313 32 16 , , PUNCT cana-5313 33 1 [ [ X cana-5313 33 2 12 12 NUM cana-5313 33 3 ] ] PUNCT cana-5313 33 4 introduced introduce VERB cana-5313 33 5 the the DET cana-5313 33 6 machine machine NOUN cana-5313 33 7 learning learning NOUN cana-5313 33 8 has have AUX cana-5313 33 9 been be AUX cana-5313 33 10 an an DET cana-5313 33 11 area area NOUN cana-5313 33 12 of of ADP cana-5313 33 13 study study NOUN cana-5313 33 14 in in ADP cana-5313 33 15 statistical statistical ADJ cana-5313 33 16 inference inference NOUN cana-5313 33 17 for for ADP cana-5313 33 18 decades decade NOUN cana-5313 33 19 , , PUNCT cana-5313 33 20 and and CCONJ cana-5313 33 21 such such ADJ cana-5313 33 22 research research NOUN cana-5313 33 23 has have AUX cana-5313 33 24 been be AUX cana-5313 33 25 to to PART cana-5313 33 26 validate validate VERB cana-5313 33 27 the the DET cana-5313 33 28 role role NOUN cana-5313 33 29 communications communication NOUN cana-5313 33 30 on on ADP cana-5313 33 31 applied apply VERB cana-5313 33 32 nonlinear nonlinear ADJ cana-5313 33 33 analysis analysis NOUN cana-5313 33 34 issn issn NOUN cana-5313 33 35 : : PUNCT cana-5313 33 36 1074 1074 NUM cana-5313 33 37 - - PUNCT cana-5313 33 38 133x 133x NUM cana-5313 33 39 vol vol NOUN cana-5313 33 40 31 31 NUM cana-5313 33 41 no no NOUN cana-5313 33 42 . . PUNCT cana-5313 34 1 8s 8s PROPN cana-5313 34 2 ( ( PUNCT cana-5313 34 3 2024 2024 NUM cana-5313 34 4 ) ) PUNCT cana-5313 34 5 937 937 NUM cana-5313 34 6 https://internationalpubls.com https://internationalpubls.com X cana-5313 34 7 in in ADP cana-5313 34 8 strengthening strengthen VERB cana-5313 34 9 model model NOUN cana-5313 34 10 reliability reliability NOUN cana-5313 34 11 and and CCONJ cana-5313 34 12 interpretability interpretability NOUN cana-5313 34 13 . . PUNCT cana-5313 35 1 it it PRON cana-5313 35 2 is be AUX cana-5313 35 3 simply simply ADV cana-5313 35 4 estimation estimation NOUN cana-5313 35 5 of of ADP cana-5313 35 6 model model NOUN cana-5313 35 7 parameters parameter NOUN cana-5313 35 8 on on ADP cana-5313 35 9 observed observed ADJ cana-5313 35 10 data datum NOUN cana-5313 35 11 and and CCONJ cana-5313 35 12 is be AUX cana-5313 35 13 a a DET cana-5313 35 14 fundamental fundamental ADJ cana-5313 35 15 aspect aspect NOUN cana-5313 35 16 of of ADP cana-5313 35 17 statistical statistical ADJ cana-5313 35 18 inference inference NOUN cana-5313 35 19 , , PUNCT cana-5313 35 20 that that PRON cana-5313 35 21 provides provide VERB cana-5313 35 22 room room NOUN cana-5313 35 23 for for ADP cana-5313 35 24 prediction prediction NOUN cana-5313 35 25 and and CCONJ cana-5313 35 26 studying study VERB cana-5313 35 27 underlying underlie VERB cana-5313 35 28 data datum NOUN cana-5313 35 29 patterns pattern NOUN cana-5313 35 30 . . PUNCT cana-5313 36 1 various various ADJ cana-5313 36 2 statistical statistical ADJ cana-5313 36 3 inference inference NOUN cana-5313 36 4 methods method NOUN cana-5313 36 5 such such ADJ cana-5313 36 6 as as ADP cana-5313 36 7 the the DET cana-5313 36 8 maximum maximum ADJ cana-5313 36 9 likelihood likelihood NOUN cana-5313 36 10 estimation estimation NOUN cana-5313 36 11 ( ( PUNCT cana-5313 36 12 mle mle NOUN cana-5313 36 13 ) ) PUNCT cana-5313 36 14 are be AUX cana-5313 36 15 contemporary contemporary ADJ cana-5313 36 16 and and CCONJ cana-5313 36 17 powerful powerful ADJ cana-5313 36 18 tools tool NOUN cana-5313 36 19 in in ADP cana-5313 36 20 machine machine NOUN cana-5313 36 21 learning learn VERB cana-5313 36 22 for for ADP cana-5313 36 23 parameter parameter NOUN cana-5313 36 24 estimation estimation NOUN cana-5313 36 25 . . PUNCT cana-5313 37 1 likelihood likelihood NOUN cana-5313 37 2 - - PUNCT cana-5313 37 3 ship ship NOUN cana-5313 37 4 function function NOUN cana-5313 37 5 is be AUX cana-5313 37 6 used use VERB cana-5313 37 7 to to PART cana-5313 37 8 maximize maximize VERB cana-5313 37 9 probability probability NOUN cana-5313 37 10 of of ADP cana-5313 37 11 generating generate VERB cana-5313 37 12 observed observe VERB cana-5313 37 13 data datum NOUN cana-5313 37 14 and and CCONJ cana-5313 37 15 mle mle PROPN cana-5313 37 16 has have AUX cana-5313 37 17 been be AUX cana-5313 37 18 applied apply VERB cana-5313 37 19 in in ADP cana-5313 37 20 a a DET cana-5313 37 21 variety variety NOUN cana-5313 37 22 of of ADP cana-5313 37 23 problems problem NOUN cana-5313 37 24 like like ADP cana-5313 37 25 linear linear PROPN cana-5313 37 26 regression regression NOUN cana-5313 37 27 , , PUNCT cana-5313 37 28 logistic logistic ADJ cana-5313 37 29 regression regression NOUN cana-5313 37 30 , , PUNCT cana-5313 37 31 classification classification NOUN cana-5313 37 32 . . PUNCT cana-5313 38 1 the the DET cana-5313 38 2 other other ADJ cana-5313 38 3 cornerstone cornerstone NOUN cana-5313 38 4 of of ADP cana-5313 38 5 statistical statistical ADJ cana-5313 38 6 methodology methodology NOUN cana-5313 38 7 is be AUX cana-5313 38 8 a a DET cana-5313 38 9 method method NOUN cana-5313 38 10 called call VERB cana-5313 38 11 bayesian bayesian NOUN cana-5313 38 12 inference inference NOUN cana-5313 38 13 that that PRON cana-5313 38 14 adds add VERB cana-5313 38 15 the the DET cana-5313 38 16 prior prior ADJ cana-5313 38 17 information information NOUN cana-5313 38 18 or or CCONJ cana-5313 38 19 prior prior ADJ cana-5313 38 20 belief belief NOUN cana-5313 38 21 in in ADP cana-5313 38 22 model model NOUN cana-5313 38 23 parameters parameter NOUN cana-5313 38 24 . . PUNCT cana-5313 39 1 using use VERB cana-5313 39 2 this this DET cana-5313 39 3 probabilistic probabilistic ADJ cana-5313 39 4 framework framework NOUN cana-5313 39 5 , , PUNCT cana-5313 39 6 we we PRON cana-5313 39 7 update update VERB cana-5313 39 8 the the DET cana-5313 39 9 prior prior ADJ cana-5313 39 10 beliefs belief NOUN cana-5313 39 11 with with ADP cana-5313 39 12 new new ADJ cana-5313 39 13 evidence evidence NOUN cana-5313 39 14 from from ADP cana-5313 39 15 data datum NOUN cana-5313 39 16 so so SCONJ cana-5313 39 17 as as SCONJ cana-5313 39 18 to to PART cana-5313 39 19 get get VERB cana-5313 39 20 a a DET cana-5313 39 21 posterior posterior ADJ cana-5313 39 22 distribution distribution NOUN cana-5313 39 23 which which PRON cana-5313 39 24 combines combine VERB cana-5313 39 25 prior prior ADJ cana-5313 39 26 knowledge knowledge NOUN cana-5313 39 27 and and CCONJ cana-5313 39 28 observed observed ADJ cana-5313 39 29 data datum NOUN cana-5313 39 30 . . PUNCT cana-5313 40 1 this this PRON cana-5313 40 2 makes make VERB cana-5313 40 3 bayesian bayesian NOUN cana-5313 40 4 methods method NOUN cana-5313 40 5 good good ADJ cana-5313 40 6 for for ADP cana-5313 40 7 machine machine NOUN cana-5313 40 8 learning learning NOUN cana-5313 40 9 when when SCONJ cana-5313 40 10 we we PRON cana-5313 40 11 have have VERB cana-5313 40 12 very very ADV cana-5313 40 13 limited limited ADJ cana-5313 40 14 or or CCONJ cana-5313 40 15 noisy noisy ADJ cana-5313 40 16 data datum NOUN cana-5313 40 17 ; ; PUNCT cana-5313 40 18 bayesian bayesian NOUN cana-5313 40 19 methods method NOUN cana-5313 40 20 seek seek VERB cana-5313 40 21 to to PART cana-5313 40 22 combine combine VERB cana-5313 40 23 prior prior ADJ cana-5313 40 24 knowledge knowledge NOUN cana-5313 40 25 into into ADP cana-5313 40 26 learning learn VERB cana-5313 40 27 . . PUNCT cana-5313 41 1 on on ADP cana-5313 41 2 the the DET cana-5313 41 3 other other ADJ cana-5313 41 4 hand hand NOUN cana-5313 41 5 , , PUNCT cana-5313 41 6 they they PRON cana-5313 41 7 enable enable VERB cana-5313 41 8 one one NUM cana-5313 41 9 to to PART cana-5313 41 10 estimate estimate VERB cana-5313 41 11 uncertainty uncertainty NOUN cana-5313 41 12 present present ADJ cana-5313 41 13 in in ADP cana-5313 41 14 model model NOUN cana-5313 41 15 predictions prediction NOUN cana-5313 41 16 , , PUNCT cana-5313 41 17 an an DET cana-5313 41 18 absolutely absolutely ADV cana-5313 41 19 essential essential ADJ cana-5313 41 20 feature feature NOUN cana-5313 41 21 in in ADP cana-5313 41 22 the the DET cana-5313 41 23 fields field NOUN cana-5313 41 24 of of ADP cana-5313 41 25 medical medical ADJ cana-5313 41 26 diagnosis diagnosis NOUN cana-5313 41 27 and and CCONJ cana-5313 41 28 autonomous autonomous ADJ cana-5313 41 29 systems system NOUN cana-5313 41 30 , , PUNCT cana-5313 41 31 where where SCONJ cana-5313 41 32 uncertainty uncertainty NOUN cana-5313 41 33 can can AUX cana-5313 41 34 cost cost VERB cana-5313 41 35 heavily heavily ADV cana-5313 41 36 on on ADP cana-5313 41 37 decisions decision NOUN cana-5313 41 38 taken take VERB cana-5313 41 39 . . PUNCT cana-5313 42 1 in in ADP cana-5313 42 2 2020 2020 NUM cana-5313 42 3 y. y. PROPN cana-5313 42 4 liu liu PROPN cana-5313 42 5 et et PROPN cana-5313 42 6 . . PUNCT cana-5313 43 1 al al PROPN cana-5313 43 2 . . PROPN cana-5313 43 3 , , PUNCT cana-5313 44 1 [ [ X cana-5313 44 2 6 6 NUM cana-5313 44 3 ] ] PUNCT cana-5313 44 4 proposed propose VERB cana-5313 44 5 the the DET cana-5313 44 6 statistical statistical ADJ cana-5313 44 7 inference inference NOUN cana-5313 44 8 in in ADP cana-5313 44 9 frequentist frequentist NOUN cana-5313 44 10 approaches approach NOUN cana-5313 44 11 to to ADP cana-5313 44 12 statistical statistical ADJ cana-5313 44 13 inference inference NOUN cana-5313 44 14 is be AUX cana-5313 44 15 based base VERB cana-5313 44 16 on on ADP cana-5313 44 17 the the DET cana-5313 44 18 idea idea NOUN cana-5313 44 19 of of ADP cana-5313 44 20 making make VERB cana-5313 44 21 decision decision NOUN cana-5313 44 22 on on ADP cana-5313 44 23 the the DET cana-5313 44 24 basis basis NOUN cana-5313 44 25 on on ADP cana-5313 44 26 the the DET cana-5313 44 27 long long ADJ cana-5313 44 28 term term NOUN cana-5313 44 29 frequency frequency NOUN cana-5313 44 30 of of ADP cana-5313 44 31 the the DET cana-5313 44 32 data data NOUN cana-5313 44 33 outcome outcome NOUN cana-5313 44 34 under under ADP cana-5313 44 35 the the DET cana-5313 44 36 repeated repeat VERB cana-5313 44 37 sample sample NOUN cana-5313 44 38 . . PUNCT cana-5313 45 1 they they PRON cana-5313 45 2 do do AUX cana-5313 45 3 not not PART cana-5313 45 4 use use VERB cana-5313 45 5 prior prior ADJ cana-5313 45 6 model model NOUN cana-5313 45 7 parameter parameter NOUN cana-5313 45 8 beliefs belief NOUN cana-5313 45 9 but but CCONJ cana-5313 45 10 provide provide VERB cana-5313 45 11 useful useful ADJ cana-5313 45 12 methods method NOUN cana-5313 45 13 for for ADP cana-5313 45 14 hypothesis hypothesis NOUN cana-5313 45 15 testing testing NOUN cana-5313 45 16 , , PUNCT cana-5313 45 17 confidence confidence NOUN cana-5313 45 18 intervals interval NOUN cana-5313 45 19 and and CCONJ cana-5313 45 20 model model NOUN cana-5313 45 21 validation validation NOUN cana-5313 45 22 . . PUNCT cana-5313 46 1 yet yet ADV cana-5313 46 2 , , PUNCT cana-5313 46 3 frequentist frequentist NOUN cana-5313 46 4 methods method NOUN cana-5313 46 5 are be AUX cana-5313 46 6 the the DET cana-5313 46 7 method method NOUN cana-5313 46 8 of of ADP cana-5313 46 9 choice choice NOUN cana-5313 46 10 to to PART cana-5313 46 11 judge judge VERB cana-5313 46 12 whether whether SCONJ cana-5313 46 13 the the DET cana-5313 46 14 parameters parameter NOUN cana-5313 46 15 of of ADP cana-5313 46 16 a a DET cana-5313 46 17 model model NOUN cana-5313 46 18 or or CCONJ cana-5313 46 19 an an DET cana-5313 46 20 algorithm algorithm NOUN cana-5313 46 21 are be AUX cana-5313 46 22 significant significant ADJ cana-5313 46 23 , , PUNCT cana-5313 46 24 to to PART cana-5313 46 25 evaluate evaluate VERB cana-5313 46 26 the the DET cana-5313 46 27 performance performance NOUN cana-5313 46 28 of of ADP cana-5313 46 29 an an DET cana-5313 46 30 algorithm algorithm NOUN cana-5313 46 31 , , PUNCT cana-5313 46 32 and and CCONJ cana-5313 46 33 to to PART cana-5313 46 34 verify verify VERB cana-5313 46 35 or or CCONJ cana-5313 46 36 refute refute VERB cana-5313 46 37 model model NOUN cana-5313 46 38 assumptions assumption NOUN cana-5313 46 39 . . PUNCT cana-5313 47 1 research research NOUN cana-5313 47 2 on on ADP cana-5313 47 3 these these DET cana-5313 47 4 statistical statistical ADJ cana-5313 47 5 techniques technique NOUN cana-5313 47 6 has have AUX cana-5313 47 7 greatly greatly ADV cana-5313 47 8 increased increase VERB cana-5313 47 9 its its PRON cana-5313 47 10 popularity popularity NOUN cana-5313 47 11 when when SCONJ cana-5313 47 12 integrating integrate VERB cana-5313 47 13 with with ADP cana-5313 47 14 machine machine NOUN cana-5313 47 15 learning learn VERB cana-5313 47 16 algorithms algorithm NOUN cana-5313 47 17 during during ADP cana-5313 47 18 recent recent ADJ cana-5313 47 19 years year NOUN cana-5313 47 20 . . PUNCT cana-5313 48 1 such such ADJ cana-5313 48 2 approach approach NOUN cana-5313 48 3 gives give VERB cana-5313 48 4 users user NOUN cana-5313 48 5 the the DET cana-5313 48 6 ability ability NOUN cana-5313 48 7 to to PART cana-5313 48 8 develop develop VERB cana-5313 48 9 robust robust ADJ cana-5313 48 10 models model NOUN cana-5313 48 11 in in ADP cana-5313 48 12 challenging challenge VERB cana-5313 48 13 datasets dataset NOUN cana-5313 48 14 with with ADP cana-5313 48 15 high high ADJ cana-5313 48 16 dimensions dimension NOUN cana-5313 48 17 . . PUNCT cana-5313 49 1 model model NOUN cana-5313 49 2 estimation estimation NOUN cana-5313 49 3 represents represent VERB cana-5313 49 4 one one NUM cana-5313 49 5 of of ADP cana-5313 49 6 the the DET cana-5313 49 7 main main ADJ cana-5313 49 8 difficulties difficulty NOUN cana-5313 49 9 in in ADP cana-5313 49 10 deep deep ADJ cana-5313 49 11 learning learning NOUN cana-5313 49 12 while while SCONJ cana-5313 49 13 the the DET cana-5313 49 14 complex complex ADJ cana-5313 49 15 task task NOUN cana-5313 49 16 requires require VERB cana-5313 49 17 proper proper ADJ cana-5313 49 18 model model NOUN cana-5313 49 19 regularization regularization NOUN cana-5313 49 20 together together ADV cana-5313 49 21 with with ADP cana-5313 49 22 uncertainty uncertainty NOUN cana-5313 49 23 measurement measurement NOUN cana-5313 49 24 in in ADP cana-5313 49 25 nonlinear nonlinear ADJ cana-5313 49 26 systems system NOUN cana-5313 49 27 . . PUNCT cana-5313 50 1 researchers researcher NOUN cana-5313 50 2 throughout throughout ADP cana-5313 50 3 the the DET cana-5313 50 4 previous previous ADJ cana-5313 50 5 years year NOUN cana-5313 50 6 connected connect VERB cana-5313 50 7 deep deep ADJ cana-5313 50 8 learning learning NOUN cana-5313 50 9 approaches approach NOUN cana-5313 50 10 with with ADP cana-5313 50 11 probabilistic probabilistic ADJ cana-5313 50 12 inference inference NOUN cana-5313 50 13 methods method NOUN cana-5313 50 14 to to PART cana-5313 50 15 create create VERB cana-5313 50 16 powerful powerful ADJ cana-5313 50 17 models model NOUN cana-5313 50 18 which which PRON cana-5313 50 19 also also ADV cana-5313 50 20 possess possess VERB cana-5313 50 21 interpretability interpretability NOUN cana-5313 50 22 features feature NOUN cana-5313 50 23 . . PUNCT cana-5313 51 1 research research NOUN cana-5313 51 2 focuses focus VERB cana-5313 51 3 on on ADP cana-5313 51 4 two two NUM cana-5313 51 5 methods method NOUN cana-5313 51 6 of of ADP cana-5313 51 7 uncertainty uncertainty NOUN cana-5313 51 8 estimation estimation NOUN cana-5313 51 9 through through ADP cana-5313 51 10 bayesian bayesian NOUN cana-5313 51 11 neural neural ADJ cana-5313 51 12 networks network NOUN cana-5313 51 13 and and CCONJ cana-5313 51 14 monte monte PROPN cana-5313 51 15 carlo carlo PROPN cana-5313 51 16 dropout dropout PROPN cana-5313 51 17 . . PUNCT cana-5313 52 1 in in ADP cana-5313 52 2 2021 2021 NUM cana-5313 52 3 a. a. NOUN cana-5313 52 4 spanos spanos PROPN cana-5313 52 5 et.al et.al PROPN cana-5313 52 6 . . PUNCT cana-5313 52 7 , , PUNCT cana-5313 52 8 [ [ X cana-5313 52 9 3 3 X cana-5313 52 10 ] ] PUNCT cana-5313 52 11 suggested suggest VERB cana-5313 52 12 the the DET cana-5313 52 13 existing exist VERB cana-5313 52 14 literature literature NOUN cana-5313 52 15 tends tend VERB cana-5313 52 16 to to PART cana-5313 52 17 focus focus VERB cana-5313 52 18 on on ADP cana-5313 52 19 the the DET cana-5313 52 20 importance importance NOUN cana-5313 52 21 of of ADP cana-5313 52 22 the the DET cana-5313 52 23 statistical statistical ADJ cana-5313 52 24 inference inference NOUN cana-5313 52 25 to to PART cana-5313 52 26 help help VERB cana-5313 52 27 in in ADP cana-5313 52 28 improving improve VERB cana-5313 52 29 machine machine NOUN cana-5313 52 30 learning learning NOUN cana-5313 52 31 models model NOUN cana-5313 52 32 through through ADP cana-5313 52 33 the the DET cana-5313 52 34 structured structured ADJ cana-5313 52 35 approach approach NOUN cana-5313 52 36 to to ADP cana-5313 52 37 the the DET cana-5313 52 38 uncertainty uncertainty NOUN cana-5313 52 39 quantification quantification NOUN cana-5313 52 40 , , PUNCT cana-5313 52 41 parameter parameter NOUN cana-5313 52 42 estimation estimation NOUN cana-5313 52 43 and and CCONJ cana-5313 52 44 model model NOUN cana-5313 52 45 validation validation NOUN cana-5313 52 46 . . PUNCT cana-5313 53 1 furthermore furthermore ADV cana-5313 53 2 , , PUNCT cana-5313 53 3 it it PRON cana-5313 53 4 is be AUX cana-5313 53 5 essential essential ADJ cana-5313 53 6 to to PART cana-5313 53 7 have have VERB cana-5313 53 8 a a DET cana-5313 53 9 growing grow VERB cana-5313 53 10 need need NOUN cana-5313 53 11 for for ADP cana-5313 53 12 frameworks framework NOUN cana-5313 53 13 that that PRON cana-5313 53 14 can can AUX cana-5313 53 15 combine combine VERB cana-5313 53 16 multiple multiple ADJ cana-5313 53 17 statistical statistical ADJ cana-5313 53 18 paradigms paradigm NOUN cana-5313 53 19 with with ADP cana-5313 53 20 ease ease NOUN cana-5313 53 21 and and CCONJ cana-5313 53 22 within within ADP cana-5313 53 23 machine machine NOUN cana-5313 53 24 learning learn VERB cana-5313 53 25 workflows workflow NOUN cana-5313 53 26 to to PART cana-5313 53 27 increase increase VERB cana-5313 53 28 model model NOUN cana-5313 53 29 flexibility flexibility NOUN cana-5313 53 30 and and CCONJ cana-5313 53 31 performance performance NOUN cana-5313 53 32 . . PUNCT cana-5313 54 1 the the DET cana-5313 54 2 key key NOUN cana-5313 54 3 to to ADP cana-5313 54 4 these these DET cana-5313 54 5 methods method NOUN cana-5313 54 6 coming come VERB cana-5313 54 7 together together ADV cana-5313 54 8 will will AUX cana-5313 54 9 be be AUX cana-5313 54 10 to to PART cana-5313 54 11 enable enable VERB cana-5313 54 12 new new ADJ cana-5313 54 13 ways way NOUN cana-5313 54 14 of of ADP cana-5313 54 15 more more ADV cana-5313 54 16 robust robust ADJ cana-5313 54 17 , , PUNCT cana-5313 54 18 more more ADV cana-5313 54 19 interpretable interpretable ADJ cana-5313 54 20 , , PUNCT cana-5313 54 21 and and CCONJ cana-5313 54 22 more more ADV cana-5313 54 23 reliable reliable ADJ cana-5313 54 24 machine machine NOUN cana-5313 54 25 learning learning NOUN cana-5313 54 26 methods method NOUN cana-5313 54 27 in in ADP cana-5313 54 28 a a DET cana-5313 54 29 number number NOUN cana-5313 54 30 of of ADP cana-5313 54 31 applications application NOUN cana-5313 54 32 . . PUNCT cana-5313 55 1 iii iii X cana-5313 55 2 . . PROPN cana-5313 55 3 proposed propose VERB cana-5313 55 4 methodology methodology NOUN cana-5313 55 5 the the DET cana-5313 55 6 integrated integrate VERB cana-5313 55 7 statistical statistical ADJ cana-5313 55 8 inference inference NOUN cana-5313 55 9 and and CCONJ cana-5313 55 10 machine machine NOUN cana-5313 55 11 learning learning NOUN cana-5313 55 12 method method NOUN cana-5313 55 13 enables enable VERB cana-5313 55 14 better well ADJ cana-5313 55 15 model model NOUN cana-5313 55 16 building building NOUN cana-5313 55 17 and and CCONJ cana-5313 55 18 both both DET cana-5313 55 19 improved improve VERB cana-5313 55 20 decision decision NOUN cana-5313 55 21 systems system NOUN cana-5313 55 22 along along ADP cana-5313 55 23 with with ADP cana-5313 55 24 quantifiable quantifiable ADJ cana-5313 55 25 uncertainties uncertainty NOUN cana-5313 55 26 . . PUNCT cana-5313 56 1 this this DET cana-5313 56 2 text text NOUN cana-5313 56 3 explains explain VERB cana-5313 56 4 a a DET cana-5313 56 5 systematic systematic ADJ cana-5313 56 6 method method NOUN cana-5313 56 7 that that PRON cana-5313 56 8 demonstrates demonstrate VERB cana-5313 56 9 how how SCONJ cana-5313 56 10 to to PART cana-5313 56 11 implement implement VERB cana-5313 56 12 mle mle PROPN cana-5313 56 13 together together ADV cana-5313 56 14 with with ADP cana-5313 56 15 bayesian bayesian NOUN cana-5313 56 16 inference inference NOUN cana-5313 56 17 communications communication NOUN cana-5313 56 18 on on ADP cana-5313 56 19 applied apply VERB cana-5313 56 20 nonlinear nonlinear ADJ cana-5313 56 21 analysis analysis NOUN cana-5313 56 22 issn issn NOUN cana-5313 56 23 : : PUNCT cana-5313 56 24 1074 1074 NUM cana-5313 56 25 - - PUNCT cana-5313 56 26 133x 133x NUM cana-5313 56 27 vol vol NOUN cana-5313 56 28 31 31 NUM cana-5313 56 29 no no NOUN cana-5313 56 30 . . PUNCT cana-5313 57 1 8s 8s PROPN cana-5313 57 2 ( ( PUNCT cana-5313 57 3 2024 2024 NUM cana-5313 57 4 ) ) PUNCT cana-5313 57 5 938 938 NUM cana-5313 57 6 https://internationalpubls.com https://internationalpubls.com X cana-5313 57 7 and and CCONJ cana-5313 57 8 frequentist frequentist NOUN cana-5313 57 9 methods method NOUN cana-5313 57 10 in in ADP cana-5313 57 11 machine machine NOUN cana-5313 57 12 learning learn VERB cana-5313 57 13 applications application NOUN cana-5313 57 14 . . PUNCT cana-5313 58 1 the the DET cana-5313 58 2 methodology methodology NOUN cana-5313 58 3 implements implement VERB cana-5313 58 4 together together ADV cana-5313 58 5 theoretical theoretical ADJ cana-5313 58 6 components component NOUN cana-5313 58 7 with with ADP cana-5313 58 8 practical practical ADJ cana-5313 58 9 elements element NOUN cana-5313 58 10 to to PART cana-5313 58 11 enhance enhance VERB cana-5313 58 12 statistical statistical ADJ cana-5313 58 13 inference inference NOUN cana-5313 58 14 which which PRON cana-5313 58 15 consequently consequently ADV cana-5313 58 16 improves improve VERB cana-5313 58 17 both both DET cana-5313 58 18 interpretability interpretability NOUN cana-5313 58 19 together together ADV cana-5313 58 20 with with ADP cana-5313 58 21 performance performance NOUN cana-5313 58 22 of of ADP cana-5313 58 23 the the DET cana-5313 58 24 overall overall ADJ cana-5313 58 25 model model NOUN cana-5313 58 26 [ [ X cana-5313 58 27 4 4 NUM cana-5313 58 28 ] ] PUNCT cana-5313 58 29 . . PUNCT cana-5313 59 1 the the DET cana-5313 59 2 initial initial ADJ cana-5313 59 3 methodology methodology NOUN cana-5313 59 4 stages stage NOUN cana-5313 59 5 specify specify VERB cana-5313 59 6 both both CCONJ cana-5313 59 7 the the DET cana-5313 59 8 data datum NOUN cana-5313 59 9 probabilistic probabilistic ADJ cana-5313 59 10 model model NOUN cana-5313 59 11 and and CCONJ cana-5313 59 12 the the DET cana-5313 59 13 selected select VERB cana-5313 59 14 inference inference NOUN cana-5313 59 15 procedure procedure NOUN cana-5313 59 16 . . PUNCT cana-5313 60 1 practices practice NOUN cana-5313 60 2 using use VERB cana-5313 60 3 mle mle PROPN cana-5313 60 4 involve involve VERB cana-5313 60 5 likelihood likelihood NOUN cana-5313 60 6 functions function NOUN cana-5313 60 7 that that PRON cana-5313 60 8 show show VERB cana-5313 60 9 probability probability NOUN cana-5313 60 10 rates rate NOUN cana-5313 60 11 of of ADP cana-5313 60 12 observed observe VERB cana-5313 60 13 data datum NOUN cana-5313 60 14 which which PRON cana-5313 60 15 depend depend VERB cana-5313 60 16 on on ADP cana-5313 60 17 model model NOUN cana-5313 60 18 parameter parameter NOUN cana-5313 60 19 values value NOUN cana-5313 60 20 [ [ X cana-5313 60 21 8 8 NUM cana-5313 60 22 ] ] PUNCT cana-5313 60 23 . . PUNCT cana-5313 61 1 suppose suppose VERB cana-5313 61 2 we we PRON cana-5313 61 3 have have VERB cana-5313 61 4 a a DET cana-5313 61 5 set set NOUN cana-5313 61 6 of of ADP cana-5313 61 7 data datum NOUN cana-5313 61 8 points point NOUN cana-5313 61 9 𝐷 𝐷 PROPN cana-5313 61 10 = = SYM cana-5313 61 11 { { PUNCT cana-5313 61 12 π‘₯1 π‘₯1 NOUN cana-5313 61 13 , , PUNCT cana-5313 61 14 π‘₯2 π‘₯2 NOUN cana-5313 61 15 , , PUNCT cana-5313 61 16 … … PUNCT cana-5313 61 17 , , PUNCT cana-5313 61 18 π‘₯𝑛 π‘₯𝑛 VERB cana-5313 61 19 } } PUNCT cana-5313 61 20 and and CCONJ cana-5313 61 21 a a DET cana-5313 61 22 model model NOUN cana-5313 61 23 with with ADP cana-5313 61 24 parameters parameter NOUN cana-5313 61 25 πœƒ. πœƒ. VERB cana-5313 61 26 the the DET cana-5313 61 27 likelihood likelihood NOUN cana-5313 61 28 function function NOUN cana-5313 61 29 𝐿(πœƒ 𝐿(πœƒ PROPN cana-5313 61 30 ∣ ∣ PROPN cana-5313 61 31 𝐷 𝐷 NOUN cana-5313 61 32 ) ) PUNCT cana-5313 61 33 is be AUX cana-5313 61 34 given give VERB cana-5313 61 35 by by ADP cana-5313 61 36 : : PUNCT cana-5313 61 37 𝐿(πœƒ 𝐿(πœƒ PROPN cana-5313 61 38 ∣ ∣ PROPN cana-5313 61 39 𝐷 𝐷 NOUN cana-5313 61 40 ) ) PUNCT cana-5313 62 1 = = NOUN cana-5313 62 2 ∏ ∏ NUM cana-5313 62 3 β€Š β€Š SPACE cana-5313 62 4 𝑛 𝑛 PRON cana-5313 62 5 𝑖=1 𝑖=1 PROPN cana-5313 62 6 𝑝(π‘₯𝑖 𝑝(π‘₯𝑖 PROPN cana-5313 62 7 ∣ ∣ PROPN cana-5313 62 8 πœƒ πœƒ NOUN cana-5313 62 9 ) ) PUNCT cana-5313 62 10 for for ADP cana-5313 62 11 computational computational ADJ cana-5313 62 12 simplicity simplicity NOUN cana-5313 62 13 , , PUNCT cana-5313 62 14 we we PRON cana-5313 62 15 often often ADV cana-5313 62 16 work work VERB cana-5313 62 17 with with ADP cana-5313 62 18 the the DET cana-5313 62 19 log log NOUN cana-5313 62 20 - - PUNCT cana-5313 62 21 likelihood likelihood NOUN cana-5313 62 22 , , PUNCT cana-5313 62 23 which which PRON cana-5313 62 24 transforms transform VERB cana-5313 62 25 the the DET cana-5313 62 26 product product NOUN cana-5313 62 27 into into ADP cana-5313 62 28 a a DET cana-5313 62 29 summation summation NOUN cana-5313 62 30 : : PUNCT cana-5313 62 31 log⁑ log⁑ PROPN cana-5313 62 32 𝐿(πœƒ 𝐿(πœƒ PROPN cana-5313 62 33 ∣ ∣ PROPN cana-5313 62 34 𝐷 𝐷 NOUN cana-5313 62 35 ) ) PUNCT cana-5313 63 1 = = NOUN cana-5313 63 2 βˆ‘ βˆ‘ PART cana-5313 63 3 β€Š β€Š SPACE cana-5313 63 4 𝑛 𝑛 PRON cana-5313 63 5 𝑖=1 𝑖=1 PROPN cana-5313 63 6 log⁑ log⁑ PROPN cana-5313 63 7 𝑝(π‘₯𝑖 𝑝(π‘₯𝑖 PROPN cana-5313 63 8 ∣ ∣ PROPN cana-5313 63 9 πœƒ πœƒ NOUN cana-5313 63 10 ) ) PUNCT cana-5313 63 11 the the DET cana-5313 63 12 goal goal NOUN cana-5313 63 13 is be AUX cana-5313 63 14 to to PART cana-5313 63 15 find find VERB cana-5313 63 16 the the DET cana-5313 63 17 value value NOUN cana-5313 63 18 of of ADP cana-5313 63 19 πœƒ πœƒ PRON cana-5313 63 20 that that PRON cana-5313 63 21 maximizes maximize VERB cana-5313 63 22 the the DET cana-5313 63 23 likelihood likelihood NOUN cana-5313 63 24 . . PUNCT cana-5313 64 1 to to PART cana-5313 64 2 do do VERB cana-5313 64 3 this this PRON cana-5313 64 4 , , PUNCT cana-5313 64 5 we we PRON cana-5313 64 6 compute compute VERB cana-5313 64 7 the the DET cana-5313 64 8 derivative derivative NOUN cana-5313 64 9 of of ADP cana-5313 64 10 the the DET cana-5313 64 11 log log NOUN cana-5313 64 12 - - PUNCT cana-5313 64 13 likelihood likelihood NOUN cana-5313 64 14 with with ADP cana-5313 64 15 respect respect NOUN cana-5313 64 16 to to ADP cana-5313 64 17 πœƒ πœƒ PROPN cana-5313 64 18 , , PUNCT cana-5313 64 19 set set VERB cana-5313 64 20 it it PRON cana-5313 64 21 equal equal ADJ cana-5313 64 22 to to ADP cana-5313 64 23 zero zero NUM cana-5313 64 24 , , PUNCT cana-5313 64 25 and and CCONJ cana-5313 64 26 solve solve VERB cana-5313 64 27 for for ADP cana-5313 64 28 𝜽 𝜽 NOUN cana-5313 64 29 : : PUNCT cana-5313 64 30 𝑑 𝑑 PROPN cana-5313 64 31 π‘‘πœƒ π‘‘πœƒ ADP cana-5313 64 32 log⁑ log⁑ X cana-5313 64 33 𝐿(πœƒ 𝐿(πœƒ PUNCT cana-5313 64 34 ∣ ∣ PROPN cana-5313 64 35 𝐷 𝐷 NOUN cana-5313 64 36 ) ) PUNCT cana-5313 64 37 = = SYM cana-5313 64 38 0 0 NUM cana-5313 64 39 bayesian bayesian NOUN cana-5313 64 40 inference inference NOUN cana-5313 64 41 functions function NOUN cana-5313 64 42 by by ADP cana-5313 64 43 implementing implement VERB cana-5313 64 44 bayes baye NOUN cana-5313 64 45 ' ' PART cana-5313 64 46 theorem theorem NOUN cana-5313 64 47 to to PART cana-5313 64 48 modify modify VERB cana-5313 64 49 original original ADJ cana-5313 64 50 parameter parameter NOUN cana-5313 64 51 model model NOUN cana-5313 64 52 assumptions assumption NOUN cana-5313 64 53 based base VERB cana-5313 64 54 on on ADP cana-5313 64 55 incoming incoming ADJ cana-5313 64 56 data datum NOUN cana-5313 64 57 measurements measurement NOUN cana-5313 64 58 . . PUNCT cana-5313 65 1 the the DET cana-5313 65 2 final final ADJ cana-5313 65 3 distribution distribution NOUN cana-5313 65 4 takes take VERB cana-5313 65 5 this this DET cana-5313 65 6 form form NOUN cana-5313 65 7 : : PUNCT cana-5313 65 8 𝑃(πœƒ 𝑃(πœƒ NUM cana-5313 65 9 ∣ ∣ PROPN cana-5313 65 10 𝐷 𝐷 NOUN cana-5313 65 11 ) ) PUNCT cana-5313 66 1 = = SYM cana-5313 66 2 𝑃(𝐷 𝑃(𝐷 PROPN cana-5313 66 3 ∣ ∣ ADJ cana-5313 66 4 πœƒ)𝑃(πœƒ πœƒ)𝑃(πœƒ NOUN cana-5313 66 5 ) ) PUNCT cana-5313 66 6 𝑃(𝐷 𝑃(𝐷 NOUN cana-5313 66 7 ) ) PUNCT cana-5313 66 8 where where SCONJ cana-5313 66 9 𝑃(πœƒ 𝑃(πœƒ NUM cana-5313 66 10 ∣ ∣ PROPN cana-5313 66 11 𝐷 𝐷 NOUN cana-5313 66 12 ) ) PUNCT cana-5313 66 13 is be AUX cana-5313 66 14 the the DET cana-5313 66 15 posterior posterior ADJ cana-5313 66 16 distribution distribution NOUN cana-5313 66 17 , , PUNCT cana-5313 66 18 𝑃(𝐷 𝑃(𝐷 NOUN cana-5313 66 19 ∣ ∣ PROPN cana-5313 66 20 πœƒ πœƒ NOUN cana-5313 66 21 ) ) PUNCT cana-5313 66 22 is be AUX cana-5313 66 23 the the DET cana-5313 66 24 likelihood likelihood NOUN cana-5313 66 25 , , PUNCT cana-5313 66 26 and and CCONJ cana-5313 66 27 𝑃(πœƒ 𝑃(πœƒ NOUN cana-5313 66 28 ) ) PUNCT cana-5313 66 29 is be AUX cana-5313 66 30 the the DET cana-5313 66 31 prior prior ADJ cana-5313 66 32 distribution distribution NOUN cana-5313 66 33 . . PUNCT cana-5313 67 1 the the DET cana-5313 67 2 goal goal NOUN cana-5313 67 3 is be AUX cana-5313 67 4 to to PART cana-5313 67 5 compute compute VERB cana-5313 67 6 the the DET cana-5313 67 7 posterior posterior ADJ cana-5313 67 8 distribution distribution NOUN cana-5313 67 9 𝑃(πœƒ 𝑃(πœƒ PART cana-5313 67 10 ∣ ∣ PROPN cana-5313 67 11 𝐷 𝐷 NOUN cana-5313 67 12 ) ) PUNCT cana-5313 67 13 , , PUNCT cana-5313 67 14 which which PRON cana-5313 67 15 can can AUX cana-5313 67 16 be be AUX cana-5313 67 17 challenging challenge VERB cana-5313 67 18 for for ADP cana-5313 67 19 complex complex ADJ cana-5313 67 20 models model NOUN cana-5313 67 21 [ [ X cana-5313 67 22 5 5 NUM cana-5313 67 23 ] ] PUNCT cana-5313 67 24 . . PUNCT cana-5313 68 1 in in ADP cana-5313 68 2 practice practice NOUN cana-5313 68 3 , , PUNCT cana-5313 68 4 we we PRON cana-5313 68 5 often often ADV cana-5313 68 6 resort resort VERB cana-5313 68 7 to to ADP cana-5313 68 8 approximation approximation NOUN cana-5313 68 9 methods method NOUN cana-5313 68 10 , , PUNCT cana-5313 68 11 such such ADJ cana-5313 68 12 as as ADP cana-5313 68 13 variational variational ADJ cana-5313 68 14 inference inference NOUN cana-5313 68 15 , , PUNCT cana-5313 68 16 to to PART cana-5313 68 17 estimate estimate VERB cana-5313 68 18 the the DET cana-5313 68 19 posterior posterior NOUN cana-5313 68 20 : : PUNCT cana-5313 68 21 𝑄(πœƒ 𝑄(πœƒ NUM cana-5313 68 22 ) ) PUNCT cana-5313 68 23 = = PUNCT cana-5313 69 1 arg⁑max arg⁑max NOUN cana-5313 69 2 πœƒ πœƒ X cana-5313 69 3 β€Š β€Š SPACE cana-5313 69 4 π”Όπ‘ž(πœƒ)[log⁑ π”Όπ‘ž(πœƒ)[log⁑ NOUN cana-5313 69 5 𝑃(𝐷 𝑃(𝐷 NOUN cana-5313 69 6 ∣ ∣ PROPN cana-5313 69 7 πœƒ πœƒ NOUN cana-5313 69 8 ) ) PUNCT cana-5313 69 9 ] ] PUNCT cana-5313 69 10 βˆ’ βˆ’ PROPN cana-5313 69 11 kl(π‘ž(πœƒ)‖𝑃(πœƒ kl(π‘ž(πœƒ)‖𝑃(πœƒ PROPN cana-5313 69 12 ) ) PUNCT cana-5313 69 13 ) ) PUNCT cana-5313 69 14 where where SCONJ cana-5313 69 15 kl(π‘ž(πœƒ)‖𝑃(πœƒ kl(π‘ž(πœƒ)‖𝑃(πœƒ NOUN cana-5313 69 16 ) ) PUNCT cana-5313 69 17 ) ) PUNCT cana-5313 69 18 is be AUX cana-5313 69 19 the the DET cana-5313 69 20 kullback kullback NOUN cana-5313 69 21 - - PUNCT cana-5313 69 22 leibler leibler NOUN cana-5313 69 23 divergence divergence NOUN cana-5313 69 24 , , PUNCT cana-5313 69 25 and and CCONJ cana-5313 69 26 π”Όπ‘ž(πœƒ π”Όπ‘ž(πœƒ NOUN cana-5313 69 27 ) ) PUNCT cana-5313 69 28 represents represent VERB cana-5313 69 29 the the DET cana-5313 69 30 expectation expectation NOUN cana-5313 69 31 with with ADP cana-5313 69 32 respect respect NOUN cana-5313 69 33 to to ADP cana-5313 69 34 the the DET cana-5313 69 35 distribution distribution NOUN cana-5313 69 36 π‘ž(πœƒ π‘ž(πœƒ PROPN cana-5313 69 37 ) ) PUNCT cana-5313 69 38 . . PUNCT cana-5313 70 1 the the DET cana-5313 70 2 frequentist frequentist NOUN cana-5313 70 3 approach approach NOUN cana-5313 70 4 , , PUNCT cana-5313 70 5 on on ADP cana-5313 70 6 the the DET cana-5313 70 7 other other ADJ cana-5313 70 8 hand hand NOUN cana-5313 70 9 , , PUNCT cana-5313 70 10 focuses focus VERB cana-5313 70 11 on on ADP cana-5313 70 12 estimating estimate VERB cana-5313 70 13 model model NOUN cana-5313 70 14 parameters parameter NOUN cana-5313 70 15 without without ADP cana-5313 70 16 incorporating incorporate VERB cana-5313 70 17 prior prior ADJ cana-5313 70 18 information information NOUN cana-5313 70 19 [ [ X cana-5313 70 20 6 6 NUM cana-5313 70 21 ] ] PUNCT cana-5313 70 22 . . PUNCT cana-5313 71 1 here here ADV cana-5313 71 2 , , PUNCT cana-5313 71 3 we we PRON cana-5313 71 4 estimate estimate VERB cana-5313 71 5 the the DET cana-5313 71 6 parameters parameter NOUN cana-5313 71 7 by by ADP cana-5313 71 8 maximizing maximize VERB cana-5313 71 9 the the DET cana-5313 71 10 likelihood likelihood NOUN cana-5313 71 11 function function NOUN cana-5313 71 12 , , PUNCT cana-5313 71 13 as as ADP cana-5313 71 14 in in ADP cana-5313 71 15 mle mle PROPN cana-5313 71 16 . . PUNCT cana-5313 72 1 in in ADP cana-5313 72 2 addition addition NOUN cana-5313 72 3 , , PUNCT cana-5313 72 4 hypothesis hypothesis NOUN cana-5313 72 5 testing testing NOUN cana-5313 72 6 is be AUX cana-5313 72 7 an an DET cana-5313 72 8 essential essential ADJ cana-5313 72 9 part part NOUN cana-5313 72 10 of of ADP cana-5313 72 11 frequentist frequentist NOUN cana-5313 72 12 inference inference NOUN cana-5313 72 13 . . PUNCT cana-5313 73 1 for for ADP cana-5313 73 2 example example NOUN cana-5313 73 3 , , PUNCT cana-5313 73 4 consider consider VERB cana-5313 73 5 the the DET cana-5313 73 6 hypothesis hypothesis NOUN cana-5313 73 7 test test NOUN cana-5313 73 8 for for ADP cana-5313 73 9 a a DET cana-5313 73 10 parameter parameter NOUN cana-5313 73 11 πœƒ πœƒ NOUN cana-5313 73 12 with with ADP cana-5313 73 13 a a DET cana-5313 73 14 null null ADJ cana-5313 73 15 hypothesis hypothesis NOUN cana-5313 73 16 𝐻0 𝐻0 PROPN cana-5313 73 17 : : PUNCT cana-5313 73 18 πœƒ πœƒ NOUN cana-5313 73 19 = = PUNCT cana-5313 73 20 πœƒ0 πœƒ0 NOUN cana-5313 73 21 and and CCONJ cana-5313 73 22 an an DET cana-5313 73 23 alternative alternative ADJ cana-5313 73 24 hypothesis hypothesis NOUN cana-5313 73 25 𝐻1 𝐻1 NOUN cana-5313 73 26 : : PUNCT cana-5313 73 27 πœƒ πœƒ X cana-5313 73 28 β‰  β‰  NOUN cana-5313 73 29 πœƒ0 πœƒ0 NOUN cana-5313 73 30 . . PUNCT cana-5313 74 1 the the DET cana-5313 74 2 test test NOUN cana-5313 74 3 statistic statistic NOUN cana-5313 74 4 is be AUX cana-5313 74 5 typically typically ADV cana-5313 74 6 based base VERB cana-5313 74 7 on on ADP cana-5313 74 8 the the DET cana-5313 74 9 likelihood likelihood NOUN cana-5313 74 10 ratio ratio NOUN cana-5313 74 11 : : PUNCT cana-5313 74 12 communications communication NOUN cana-5313 74 13 on on ADP cana-5313 74 14 applied apply VERB cana-5313 74 15 nonlinear nonlinear ADJ cana-5313 74 16 analysis analysis NOUN cana-5313 74 17 issn issn NOUN cana-5313 74 18 : : PUNCT cana-5313 74 19 1074 1074 NUM cana-5313 74 20 - - PUNCT cana-5313 74 21 133x 133x NUM cana-5313 74 22 vol vol NOUN cana-5313 74 23 31 31 NUM cana-5313 74 24 no no NOUN cana-5313 74 25 . . PUNCT cana-5313 75 1 8s 8s PROPN cana-5313 75 2 ( ( PUNCT cana-5313 75 3 2024 2024 NUM cana-5313 75 4 ) ) PUNCT cana-5313 75 5 939 939 NUM cana-5313 75 6 https://internationalpubls.com https://internationalpubls.com X cana-5313 75 7 Ξ» Ξ» PROPN cana-5313 75 8 = = SYM cana-5313 75 9 𝐿( 𝐿( PROPN cana-5313 75 10 οΏ½ οΏ½ PROPN cana-5313 75 11 Μ‚ Μ‚ NOUN cana-5313 75 12 οΏ½ οΏ½ NOUN cana-5313 75 13 0 0 NUM cana-5313 75 14 ∣ ∣ PROPN cana-5313 75 15 𝐷 𝐷 PROPN cana-5313 75 16 ) ) PUNCT cana-5313 75 17 𝐿( 𝐿( NOUN cana-5313 75 18 οΏ½ οΏ½ PROPN cana-5313 75 19 Μ‚ Μ‚ VERB cana-5313 75 20 οΏ½ οΏ½ NOUN cana-5313 75 21 1 1 NUM cana-5313 75 22 ∣ ∣ PROPN cana-5313 75 23 𝐷 𝐷 NOUN cana-5313 75 24 ) ) PUNCT cana-5313 75 25 where where SCONJ cana-5313 75 26 οΏ½ οΏ½ PROPN cana-5313 75 27 Μ‚ Μ‚ SYM cana-5313 75 28 οΏ½ οΏ½ NOUN cana-5313 75 29 0 0 NUM cana-5313 75 30 and and CCONJ cana-5313 75 31 οΏ½ οΏ½ PROPN cana-5313 75 32 Μ‚ Μ‚ VERB cana-5313 75 33 οΏ½ οΏ½ NOUN cana-5313 75 34 1 1 NUM cana-5313 75 35 are be AUX cana-5313 75 36 the the DET cana-5313 75 37 estimates estimate NOUN cana-5313 75 38 under under ADP cana-5313 75 39 the the DET cana-5313 75 40 null null ADJ cana-5313 75 41 and and CCONJ cana-5313 75 42 alternative alternative ADJ cana-5313 75 43 hypotheses hypothesis NOUN cana-5313 75 44 , , PUNCT cana-5313 75 45 respectively respectively ADV cana-5313 75 46 . . PUNCT cana-5313 76 1 in in ADP cana-5313 76 2 practice practice NOUN cana-5313 76 3 , , PUNCT cana-5313 76 4 we we PRON cana-5313 76 5 integrate integrate VERB cana-5313 76 6 these these DET cana-5313 76 7 statistical statistical ADJ cana-5313 76 8 methods method NOUN cana-5313 76 9 into into ADP cana-5313 76 10 machine machine NOUN cana-5313 76 11 learning learning NOUN cana-5313 76 12 workflows workflow NOUN cana-5313 76 13 by by ADP cana-5313 76 14 using use VERB cana-5313 76 15 them they PRON cana-5313 76 16 to to PART cana-5313 76 17 estimate estimate VERB cana-5313 76 18 parameters parameter NOUN cana-5313 76 19 , , PUNCT cana-5313 76 20 validate validate ADJ cana-5313 76 21 models model NOUN cana-5313 76 22 , , PUNCT cana-5313 76 23 and and CCONJ cana-5313 76 24 quantify quantify VERB cana-5313 76 25 uncertainty uncertainty NOUN cana-5313 76 26 . . PUNCT cana-5313 77 1 for for ADP cana-5313 77 2 example example NOUN cana-5313 77 3 , , PUNCT cana-5313 77 4 in in ADP cana-5313 77 5 a a DET cana-5313 77 6 classification classification NOUN cana-5313 77 7 problem problem NOUN cana-5313 77 8 using use VERB cana-5313 77 9 logistic logistic ADJ cana-5313 77 10 regression regression NOUN cana-5313 77 11 , , PUNCT cana-5313 77 12 we we PRON cana-5313 77 13 can can AUX cana-5313 77 14 estimate estimate VERB cana-5313 77 15 the the DET cana-5313 77 16 regression regression NOUN cana-5313 77 17 coefficients coefficient VERB cana-5313 77 18 𝛽 𝛽 NOUN cana-5313 77 19 by by ADP cana-5313 77 20 maximizing maximize VERB cana-5313 77 21 the the DET cana-5313 77 22 likelihood likelihood NOUN cana-5313 77 23 function function NOUN cana-5313 77 24 for for ADP cana-5313 77 25 the the DET cana-5313 77 26 logistic logistic ADJ cana-5313 77 27 model model NOUN cana-5313 77 28 : : PUNCT cana-5313 77 29 𝐿(𝛽 𝐿(𝛽 NUM cana-5313 77 30 ∣ ∣ PROPN cana-5313 77 31 𝐷 𝐷 NOUN cana-5313 77 32 ) ) PUNCT cana-5313 77 33 = = SYM cana-5313 78 1 ∏ ∏ NUM cana-5313 78 2 β€Š β€Š SPACE cana-5313 78 3 𝑛 𝑛 PRON cana-5313 78 4 𝑖=1 𝑖=1 PROPN cana-5313 78 5 ( ( PUNCT cana-5313 78 6 𝑝(π‘₯𝑖 𝑝(π‘₯𝑖 PROPN cana-5313 78 7 ∣ ∣ PROPN cana-5313 78 8 𝛽 𝛽 PROPN cana-5313 78 9 ) ) PUNCT cana-5313 78 10 𝑦𝑖(1 𝑦𝑖(1 PROPN cana-5313 78 11 βˆ’ βˆ’ PROPN cana-5313 78 12 𝑝(π‘₯𝑖 𝑝(π‘₯𝑖 PROPN cana-5313 78 13 ∣ ∣ PROPN cana-5313 78 14 𝛽 𝛽 NOUN cana-5313 78 15 ) ) PUNCT cana-5313 78 16 ) ) PUNCT cana-5313 79 1 1βˆ’π‘¦π‘– 1βˆ’π‘¦π‘– NUM cana-5313 79 2 ) ) PUNCT cana-5313 79 3 where where SCONJ cana-5313 79 4 𝑦𝑖 𝑦𝑖 PROPN cana-5313 79 5 is be AUX cana-5313 79 6 the the DET cana-5313 79 7 observed observed ADJ cana-5313 79 8 class class NOUN cana-5313 79 9 label label NOUN cana-5313 79 10 , , PUNCT cana-5313 79 11 and and CCONJ cana-5313 79 12 𝑝(π‘₯𝑖 𝑝(π‘₯𝑖 NUM cana-5313 79 13 ∣ ∣ PROPN cana-5313 79 14 𝛽 𝛽 NOUN cana-5313 79 15 ) ) PUNCT cana-5313 79 16 is be AUX cana-5313 79 17 the the DET cana-5313 79 18 probability probability NOUN cana-5313 79 19 of of ADP cana-5313 79 20 the the DET cana-5313 79 21 𝑖-th 𝑖-th PROPN cana-5313 79 22 observation observation NOUN cana-5313 79 23 belonging belong VERB cana-5313 79 24 to to ADP cana-5313 79 25 the the DET cana-5313 79 26 positive positive ADJ cana-5313 79 27 class class NOUN cana-5313 79 28 . . PUNCT cana-5313 80 1 the the DET cana-5313 80 2 log log NOUN cana-5313 80 3 - - PUNCT cana-5313 80 4 likelihood likelihood NOUN cana-5313 80 5 for for ADP cana-5313 80 6 logistic logistic ADJ cana-5313 80 7 regression regression NOUN cana-5313 80 8 is be AUX cana-5313 80 9 : : PUNCT cana-5313 80 10 log⁑ log⁑ X cana-5313 80 11 𝐿(𝛽 𝐿(𝛽 NUM cana-5313 80 12 ∣ ∣ PROPN cana-5313 80 13 𝐷 𝐷 NOUN cana-5313 80 14 ) ) PUNCT cana-5313 80 15 = = PUNCT cana-5313 81 1 βˆ‘ βˆ‘ PUNCT cana-5313 81 2 β€Š β€Š SPACE cana-5313 81 3 𝑛 𝑛 PRON cana-5313 81 4 𝑖=1 𝑖=1 PROPN cana-5313 82 1 [ [ X cana-5313 82 2 𝑦𝑖log⁑ 𝑦𝑖log⁑ ADJ cana-5313 82 3 𝑝(π‘₯𝑖 𝑝(π‘₯𝑖 PROPN cana-5313 82 4 ∣ ∣ PROPN cana-5313 82 5 𝛽 𝛽 NOUN cana-5313 82 6 ) ) PUNCT cana-5313 83 1 + + CCONJ cana-5313 83 2 ( ( PUNCT cana-5313 83 3 1 1 NUM cana-5313 83 4 βˆ’ βˆ’ NOUN cana-5313 83 5 𝑦𝑖)log⁑(1 𝑦𝑖)log⁑(1 NOUN cana-5313 83 6 βˆ’ βˆ’ PROPN cana-5313 83 7 𝑝(π‘₯𝑖 𝑝(π‘₯𝑖 PROPN cana-5313 83 8 ∣ ∣ PROPN cana-5313 83 9 𝛽 𝛽 NOUN cana-5313 83 10 ) ) PUNCT cana-5313 83 11 ) ) PUNCT cana-5313 83 12 ] ] PUNCT cana-5313 84 1 maximizing maximize VERB cana-5313 84 2 the the DET cana-5313 84 3 log log NOUN cana-5313 84 4 - - PUNCT cana-5313 84 5 likelihood likelihood NOUN cana-5313 84 6 leads lead VERB cana-5313 84 7 to to ADP cana-5313 84 8 the the DET cana-5313 84 9 estimation estimation NOUN cana-5313 84 10 of of ADP cana-5313 84 11 the the DET cana-5313 84 12 coefficients coefficient NOUN cana-5313 84 13 𝛽 𝛽 PROPN cana-5313 84 14 , , PUNCT cana-5313 84 15 which which PRON cana-5313 84 16 are be AUX cana-5313 84 17 critical critical ADJ cana-5313 84 18 for for ADP cana-5313 84 19 making make VERB cana-5313 84 20 predictions prediction NOUN cana-5313 84 21 . . PUNCT cana-5313 85 1 the the DET cana-5313 85 2 integration integration NOUN cana-5313 85 3 of of ADP cana-5313 85 4 bayesian bayesian NOUN cana-5313 85 5 methods method NOUN cana-5313 85 6 can can AUX cana-5313 85 7 be be AUX cana-5313 85 8 beneficial beneficial ADJ cana-5313 85 9 in in ADP cana-5313 85 10 cases case NOUN cana-5313 85 11 where where SCONJ cana-5313 85 12 we we PRON cana-5313 85 13 want want VERB cana-5313 85 14 to to PART cana-5313 85 15 quantify quantify VERB cana-5313 85 16 the the DET cana-5313 85 17 uncertainty uncertainty NOUN cana-5313 85 18 in in ADP cana-5313 85 19 the the DET cana-5313 85 20 model model NOUN cana-5313 85 21 's 's PART cana-5313 85 22 predictions prediction NOUN cana-5313 85 23 . . PUNCT cana-5313 86 1 for for ADP cana-5313 86 2 example example NOUN cana-5313 86 3 , , PUNCT cana-5313 86 4 in in ADP cana-5313 86 5 bayesian bayesian NOUN cana-5313 86 6 linear linear PROPN cana-5313 86 7 regression regression NOUN cana-5313 86 8 , , PUNCT cana-5313 86 9 the the DET cana-5313 86 10 posterior posterior ADJ cana-5313 86 11 distribution distribution NOUN cana-5313 86 12 of of ADP cana-5313 86 13 the the DET cana-5313 86 14 parameters parameter NOUN cana-5313 86 15 𝛽 𝛽 NOUN cana-5313 86 16 given give VERB cana-5313 86 17 the the DET cana-5313 86 18 data data NOUN cana-5313 86 19 is be AUX cana-5313 86 20 : : PUNCT cana-5313 86 21 𝑃(𝛽 𝑃(𝛽 PROPN cana-5313 86 22 ∣ ∣ PROPN cana-5313 86 23 𝐷 𝐷 NOUN cana-5313 86 24 ) ) PUNCT cana-5313 87 1 = = SYM cana-5313 87 2 𝑃(𝐷 𝑃(𝐷 PROPN cana-5313 87 3 ∣ ∣ PROPN cana-5313 87 4 𝛽)𝑃(𝛽 𝛽)𝑃(𝛽 PROPN cana-5313 87 5 ) ) PUNCT cana-5313 87 6 𝑃(𝐷 𝑃(𝐷 PROPN cana-5313 87 7 ) ) PUNCT cana-5313 87 8 this this PRON cana-5313 87 9 allows allow VERB cana-5313 87 10 for for ADP cana-5313 87 11 the the DET cana-5313 87 12 computation computation NOUN cana-5313 87 13 of of ADP cana-5313 87 14 a a DET cana-5313 87 15 credible credible ADJ cana-5313 87 16 interval interval NOUN cana-5313 87 17 for for ADP cana-5313 87 18 the the DET cana-5313 87 19 parameters parameter NOUN cana-5313 87 20 , , PUNCT cana-5313 87 21 providing provide VERB cana-5313 87 22 insight insight NOUN cana-5313 87 23 into into ADP cana-5313 87 24 the the DET cana-5313 87 25 uncertainty uncertainty NOUN cana-5313 87 26 associated associate VERB cana-5313 87 27 with with ADP cana-5313 87 28 the the DET cana-5313 87 29 parameter parameter NOUN cana-5313 87 30 estimates estimate NOUN cana-5313 87 31 . . PUNCT cana-5313 88 1 the the DET cana-5313 88 2 validation validation NOUN cana-5313 88 3 process process NOUN cana-5313 88 4 involves involve VERB cana-5313 88 5 using use VERB cana-5313 88 6 cross cross ADJ cana-5313 88 7 - - ADJ cana-5313 88 8 validation validation ADJ cana-5313 88 9 techniques technique NOUN cana-5313 88 10 as as ADP cana-5313 88 11 another another DET cana-5313 88 12 approach approach NOUN cana-5313 88 13 . . PUNCT cana-5313 89 1 the the DET cana-5313 89 2 data datum NOUN cana-5313 89 3 divides divide VERB cana-5313 89 4 into into ADP cana-5313 89 5 k k PROPN cana-5313 89 6 subsets subset NOUN cana-5313 89 7 when when SCONJ cana-5313 89 8 performing perform VERB cana-5313 89 9 k k PROPN cana-5313 89 10 -fold -fold PROPN cana-5313 89 11 cross cross NOUN cana-5313 89 12 - - NOUN cana-5313 89 13 validation validation NOUN cana-5313 89 14 which which PRON cana-5313 89 15 allows allow VERB cana-5313 89 16 model model NOUN cana-5313 89 17 training training NOUN cana-5313 89 18 and and CCONJ cana-5313 89 19 evaluation evaluation NOUN cana-5313 89 20 on on ADP cana-5313 89 21 each each DET cana-5313 89 22 subset subset NOUN cana-5313 89 23 . . PUNCT cana-5313 90 1 the the DET cana-5313 90 2 performance performance NOUN cana-5313 90 3 metrics metric NOUN cana-5313 90 4 obtain obtain VERB cana-5313 90 5 their their PRON cana-5313 90 6 average average ADJ cana-5313 90 7 values value NOUN cana-5313 90 8 across across ADP cana-5313 90 9 the the DET cana-5313 90 10 folds fold NOUN cana-5313 90 11 to to PART cana-5313 90 12 calculate calculate VERB cana-5313 90 13 model model NOUN cana-5313 90 14 generalization generalization NOUN cana-5313 90 15 statistics statistic NOUN cana-5313 90 16 . . PUNCT cana-5313 91 1 accuracy accuracy NOUN cana-5313 91 2 and and CCONJ cana-5313 91 3 precision precision NOUN cana-5313 91 4 serve serve VERB cana-5313 91 5 with with ADP cana-5313 91 6 recall recall NOUN cana-5313 91 7 and and CCONJ cana-5313 91 8 the the DET cana-5313 91 9 f1 f1 NOUN cana-5313 91 10 - - PUNCT cana-5313 91 11 score score NOUN cana-5313 91 12 for for ADP cana-5313 91 13 measuring measure VERB cana-5313 91 14 the the DET cana-5313 91 15 performance performance NOUN cana-5313 91 16 of of ADP cana-5313 91 17 a a DET cana-5313 91 18 model model NOUN cana-5313 91 19 . . PUNCT cana-5313 92 1 next next ADV cana-5313 92 2 , , PUNCT cana-5313 92 3 we we PRON cana-5313 92 4 apply apply VERB cana-5313 92 5 these these DET cana-5313 92 6 methods method NOUN cana-5313 92 7 to to ADP cana-5313 92 8 a a DET cana-5313 92 9 deep deep ADJ cana-5313 92 10 learning learning NOUN cana-5313 92 11 scenario scenario NOUN cana-5313 92 12 . . PUNCT cana-5313 93 1 for for ADP cana-5313 93 2 deep deep ADJ cana-5313 93 3 learning learning NOUN cana-5313 93 4 models model NOUN cana-5313 93 5 , , PUNCT cana-5313 93 6 uncertainty uncertainty NOUN cana-5313 93 7 quantification quantification NOUN cana-5313 93 8 can can AUX cana-5313 93 9 be be AUX cana-5313 93 10 achieved achieve VERB cana-5313 93 11 using use VERB cana-5313 93 12 techniques technique NOUN cana-5313 93 13 such such ADJ cana-5313 93 14 as as ADP cana-5313 93 15 monte monte PROPN cana-5313 93 16 carlo carlo PROPN cana-5313 93 17 dropout dropout PROPN cana-5313 93 18 , , PUNCT cana-5313 93 19 where where SCONJ cana-5313 93 20 dropout dropout NOUN cana-5313 93 21 is be AUX cana-5313 93 22 applied apply VERB cana-5313 93 23 at at ADP cana-5313 93 24 inference inference NOUN cana-5313 93 25 time time NOUN cana-5313 93 26 to to PART cana-5313 93 27 approximate approximate VERB cana-5313 93 28 bayesian bayesian NOUN cana-5313 93 29 posterior posterior ADJ cana-5313 93 30 distributions distribution NOUN cana-5313 93 31 . . PUNCT cana-5313 94 1 the the DET cana-5313 94 2 dropout dropout NOUN cana-5313 94 3 probability probability NOUN cana-5313 94 4 𝑝 𝑝 NOUN cana-5313 94 5 for for ADP cana-5313 94 6 a a DET cana-5313 94 7 neuron neuron NOUN cana-5313 94 8 is be AUX cana-5313 94 9 modeled model VERB cana-5313 94 10 as as ADP cana-5313 94 11 a a DET cana-5313 94 12 bernoulli bernoulli NOUN cana-5313 94 13 random random ADJ cana-5313 94 14 variable variable NOUN cana-5313 94 15 : : PUNCT cana-5313 94 16 𝑧𝑖 𝑧𝑖 NOUN cana-5313 94 17 = = ADJ cana-5313 94 18 1{bernoulli 1{bernoulli X cana-5313 94 19 ( ( PUNCT cana-5313 94 20 𝑝 𝑝 NOUN cana-5313 94 21 ) ) PUNCT cana-5313 94 22 } } PUNCT cana-5313 94 23 β‹… β‹… PROPN cana-5313 94 24 𝑀𝑖 𝑀𝑖 ADP cana-5313 94 25 where where SCONJ cana-5313 94 26 𝑧𝑖 𝑧𝑖 PRON cana-5313 94 27 is be AUX cana-5313 94 28 the the DET cana-5313 94 29 output output NOUN cana-5313 94 30 of of ADP cana-5313 94 31 the the DET cana-5313 94 32 𝑖-th 𝑖-th PROPN cana-5313 94 33 neuron neuron NOUN cana-5313 94 34 , , PUNCT cana-5313 94 35 and and CCONJ cana-5313 94 36 𝑀𝑖 𝑀𝑖 PRON cana-5313 94 37 is be AUX cana-5313 94 38 the the DET cana-5313 94 39 weight weight NOUN cana-5313 94 40 . . PUNCT cana-5313 95 1 this this PRON cana-5313 95 2 allows allow VERB cana-5313 95 3 the the DET cana-5313 95 4 model model NOUN cana-5313 95 5 to to PART cana-5313 95 6 estimate estimate VERB cana-5313 95 7 uncertainty uncertainty NOUN cana-5313 95 8 in in ADP cana-5313 95 9 its its PRON cana-5313 95 10 predictions prediction NOUN cana-5313 95 11 by by ADP cana-5313 95 12 generating generate VERB cana-5313 95 13 multiple multiple ADJ cana-5313 95 14 outputs output NOUN cana-5313 95 15 for for ADP cana-5313 95 16 the the DET cana-5313 95 17 same same ADJ cana-5313 95 18 input input NOUN cana-5313 95 19 . . PUNCT cana-5313 96 1 communications communication NOUN cana-5313 96 2 on on ADP cana-5313 96 3 applied apply VERB cana-5313 96 4 nonlinear nonlinear ADJ cana-5313 96 5 analysis analysis NOUN cana-5313 96 6 issn issn NOUN cana-5313 96 7 : : PUNCT cana-5313 96 8 1074 1074 NUM cana-5313 96 9 - - PUNCT cana-5313 96 10 133x 133x NUM cana-5313 96 11 vol vol NOUN cana-5313 96 12 31 31 NUM cana-5313 96 13 no no NOUN cana-5313 96 14 . . PUNCT cana-5313 97 1 8s 8s PROPN cana-5313 97 2 ( ( PUNCT cana-5313 97 3 2024 2024 NUM cana-5313 97 4 ) ) PUNCT cana-5313 97 5 940 940 NUM cana-5313 97 6 https://internationalpubls.com https://internationalpubls.com X cana-5313 97 7 in in ADP cana-5313 97 8 the the DET cana-5313 97 9 final final ADJ cana-5313 97 10 step step NOUN cana-5313 97 11 , , PUNCT cana-5313 97 12 model model NOUN cana-5313 97 13 performance performance NOUN cana-5313 97 14 is be AUX cana-5313 97 15 evaluated evaluate VERB cana-5313 97 16 using use VERB cana-5313 97 17 a a DET cana-5313 97 18 set set NOUN cana-5313 97 19 of of ADP cana-5313 97 20 performance performance NOUN cana-5313 97 21 metrics metric NOUN cana-5313 97 22 , , PUNCT cana-5313 97 23 which which PRON cana-5313 97 24 may may AUX cana-5313 97 25 include include VERB cana-5313 97 26 both both DET cana-5313 97 27 frequentist frequentist NOUN cana-5313 97 28 and and CCONJ cana-5313 97 29 bayesian bayesian NOUN cana-5313 97 30 methods method NOUN cana-5313 97 31 . . PUNCT cana-5313 98 1 for for ADP cana-5313 98 2 example example NOUN cana-5313 98 3 , , PUNCT cana-5313 98 4 the the DET cana-5313 98 5 likelihood likelihood NOUN cana-5313 98 6 ratio ratio NOUN cana-5313 98 7 test test NOUN cana-5313 98 8 can can AUX cana-5313 98 9 be be AUX cana-5313 98 10 used use VERB cana-5313 98 11 to to PART cana-5313 98 12 compare compare VERB cana-5313 98 13 different different ADJ cana-5313 98 14 models model NOUN cana-5313 98 15 based base VERB cana-5313 98 16 on on ADP cana-5313 98 17 their their PRON cana-5313 98 18 log log NOUN cana-5313 98 19 - - PUNCT cana-5313 98 20 likelihoods likelihood NOUN cana-5313 98 21 : : PUNCT cana-5313 98 22 Ξ» Ξ» X cana-5313 98 23 = = SYM cana-5313 98 24 2 2 NUM cana-5313 98 25 ( ( PUNCT cana-5313 98 26 log⁑ log⁑ PROPN cana-5313 98 27 𝐿( 𝐿( PROPN cana-5313 98 28 οΏ½ οΏ½ PROPN cana-5313 98 29 Μ‚ Μ‚ VERB cana-5313 98 30 οΏ½ οΏ½ NOUN cana-5313 98 31 1 1 NUM cana-5313 98 32 ∣ ∣ PROPN cana-5313 98 33 𝐷 𝐷 NOUN cana-5313 98 34 ) ) PUNCT cana-5313 98 35 βˆ’ βˆ’ PROPN cana-5313 98 36 log⁑ log⁑ PROPN cana-5313 98 37 𝐿( 𝐿( PROPN cana-5313 98 38 οΏ½ οΏ½ PROPN cana-5313 98 39 Μ‚ Μ‚ PROPN cana-5313 98 40 οΏ½ οΏ½ PROPN cana-5313 98 41 0 0 NUM cana-5313 98 42 ∣ ∣ PROPN cana-5313 98 43 𝐷 𝐷 PROPN cana-5313 98 44 ) ) PUNCT cana-5313 98 45 ) ) PUNCT cana-5313 99 1 this this DET cana-5313 99 2 statistical statistical ADJ cana-5313 99 3 test test NOUN cana-5313 99 4 helps helps AUX cana-5313 99 5 determine determine VERB cana-5313 99 6 whether whether SCONJ cana-5313 99 7 one one NUM cana-5313 99 8 model model NOUN cana-5313 99 9 significantly significantly ADV cana-5313 99 10 outperforms outperform VERB cana-5313 99 11 another another PRON cana-5313 99 12 . . PUNCT cana-5313 100 1 a a DET cana-5313 100 2 flowchart flowchart NOUN cana-5313 100 3 summarizing summarize VERB cana-5313 100 4 the the DET cana-5313 100 5 methodology methodology NOUN cana-5313 100 6 is be AUX cana-5313 100 7 shown show VERB cana-5313 100 8 below below ADP cana-5313 100 9 : : PUNCT cana-5313 100 10 figure figure NOUN cana-5313 100 11 1 1 NUM cana-5313 100 12 : : PUNCT cana-5313 100 13 proposed propose VERB cana-5313 100 14 framework framework NOUN cana-5313 100 15 integrating integrate VERB cana-5313 100 16 statistical statistical ADJ cana-5313 100 17 inference inference NOUN cana-5313 100 18 into into ADP cana-5313 100 19 machine machine NOUN cana-5313 100 20 learning learn VERB cana-5313 100 21 pipelines pipeline NOUN cana-5313 100 22 iv iv NOUN cana-5313 100 23 . . PUNCT cana-5313 100 24 result result PROPN cana-5313 100 25 & & CCONJ cana-5313 100 26 discussions discussion NOUN cana-5313 100 27 the the DET cana-5313 100 28 section section NOUN cana-5313 100 29 presents present VERB cana-5313 100 30 real real ADJ cana-5313 100 31 - - PUNCT cana-5313 100 32 world world NOUN cana-5313 100 33 dataset dataset NOUN cana-5313 100 34 application application NOUN cana-5313 100 35 results result NOUN cana-5313 100 36 along along ADP cana-5313 100 37 with with ADP cana-5313 100 38 traditional traditional ADJ cana-5313 100 39 machine machine NOUN cana-5313 100 40 learning learning NOUN cana-5313 100 41 method method NOUN cana-5313 100 42 testing testing NOUN cana-5313 100 43 comparisons comparison NOUN cana-5313 100 44 . . PUNCT cana-5313 101 1 the the DET cana-5313 101 2 main main ADJ cana-5313 101 3 priority priority NOUN cana-5313 101 4 here here ADV cana-5313 101 5 is be AUX cana-5313 101 6 how how SCONJ cana-5313 101 7 well well ADV cana-5313 101 8 the the DET cana-5313 101 9 statistical statistical ADJ cana-5313 101 10 methods method NOUN cana-5313 101 11 perform perform VERB cana-5313 101 12 in in ADP cana-5313 101 13 terms term NOUN cana-5313 101 14 of of ADP cana-5313 101 15 accuracy accuracy NOUN cana-5313 101 16 alongside alongside ADP cana-5313 101 17 their their PRON cana-5313 101 18 robustness robustness NOUN cana-5313 101 19 criteria criterion NOUN cana-5313 101 20 and and CCONJ cana-5313 101 21 their their PRON cana-5313 101 22 ability ability NOUN cana-5313 101 23 to to PART cana-5313 101 24 measure measure VERB cana-5313 101 25 uncertainty uncertainty NOUN cana-5313 101 26 [ [ X cana-5313 101 27 7 7 NUM cana-5313 101 28 ] ] PUNCT cana-5313 101 29 . . PUNCT cana-5313 102 1 a a DET cana-5313 102 2 binary binary ADJ cana-5313 102 3 classification classification NOUN cana-5313 102 4 problem problem NOUN cana-5313 102 5 serves serve VERB cana-5313 102 6 as as ADP cana-5313 102 7 the the DET cana-5313 102 8 subject subject NOUN cana-5313 102 9 for for ADP cana-5313 102 10 applying apply VERB cana-5313 102 11 mle mle NOUN cana-5313 102 12 and and CCONJ cana-5313 102 13 bayesian bayesian NOUN cana-5313 102 14 methods method NOUN cana-5313 102 15 within within ADP cana-5313 102 16 this this DET cana-5313 102 17 experiment experiment NOUN cana-5313 102 18 . . PUNCT cana-5313 103 1 the the DET cana-5313 103 2 system system NOUN cana-5313 103 3 used use VERB cana-5313 103 4 10,000 10,000 NUM cana-5313 103 5 examples example NOUN cana-5313 103 6 with with ADP cana-5313 103 7 20 20 NUM cana-5313 103 8 variables variable NOUN cana-5313 103 9 divided divide VERB cana-5313 103 10 into into ADP cana-5313 103 11 training training NOUN cana-5313 103 12 data datum NOUN cana-5313 103 13 and and CCONJ cana-5313 103 14 test test NOUN cana-5313 103 15 data datum NOUN cana-5313 103 16 . . PUNCT cana-5313 104 1 during during ADP cana-5313 104 2 mle mle PROPN cana-5313 104 3 execution execution NOUN cana-5313 104 4 we we PRON cana-5313 104 5 calculated calculate VERB cana-5313 104 6 the the DET cana-5313 104 7 model model NOUN cana-5313 104 8 parameters parameter NOUN cana-5313 104 9 before before ADP cana-5313 104 10 implementing implement VERB cana-5313 104 11 logistic logistic ADJ cana-5313 104 12 regression regression NOUN cana-5313 104 13 modeling modeling NOUN cana-5313 104 14 . . PUNCT cana-5313 105 1 a a DET cana-5313 105 2 probabilistic probabilistic ADJ cana-5313 105 3 model model NOUN cana-5313 105 4 implemented implement VERB cana-5313 105 5 the the DET cana-5313 105 6 bayesian bayesian NOUN cana-5313 105 7 approach approach NOUN cana-5313 105 8 to to PART cana-5313 105 9 merge merge VERB cana-5313 105 10 prior prior ADJ cana-5313 105 11 information information NOUN cana-5313 105 12 regarding regard VERB cana-5313 105 13 the the DET cana-5313 105 14 data datum NOUN cana-5313 105 15 distribution distribution NOUN cana-5313 105 16 . . PUNCT cana-5313 106 1 these these DET cana-5313 106 2 two two NUM cana-5313 106 3 communications communication NOUN cana-5313 106 4 on on ADP cana-5313 106 5 applied apply VERB cana-5313 106 6 nonlinear nonlinear ADJ cana-5313 106 7 analysis analysis NOUN cana-5313 106 8 issn issn NOUN cana-5313 106 9 : : PUNCT cana-5313 106 10 1074 1074 NUM cana-5313 106 11 - - PUNCT cana-5313 106 12 133x 133x NUM cana-5313 106 13 vol vol NOUN cana-5313 106 14 31 31 NUM cana-5313 106 15 no no NOUN cana-5313 106 16 . . PUNCT cana-5313 107 1 8s 8s PROPN cana-5313 107 2 ( ( PUNCT cana-5313 107 3 2024 2024 NUM cana-5313 107 4 ) ) PUNCT cana-5313 107 5 941 941 NUM cana-5313 107 6 https://internationalpubls.com https://internationalpubls.com X cana-5313 107 7 methods method NOUN cana-5313 107 8 generated generate VERB cana-5313 107 9 results result NOUN cana-5313 107 10 that that PRON cana-5313 107 11 were be AUX cana-5313 107 12 compared compare VERB cana-5313 107 13 with with ADP cana-5313 107 14 the the DET cana-5313 107 15 traditional traditional ADJ cana-5313 107 16 logistic logistic ADJ cana-5313 107 17 regression regression NOUN cana-5313 107 18 model model NOUN cana-5313 107 19 that that PRON cana-5313 107 20 lacked lack VERB cana-5313 107 21 statistical statistical ADJ cana-5313 107 22 inference inference NOUN cana-5313 107 23 methods method NOUN cana-5313 107 24 . . PUNCT cana-5313 108 1 the the DET cana-5313 108 2 visual visual ADJ cana-5313 108 3 representation representation NOUN cana-5313 108 4 of of ADP cana-5313 108 5 accuracy accuracy NOUN cana-5313 108 6 scores score NOUN cana-5313 108 7 appears appear VERB cana-5313 108 8 in in ADP cana-5313 108 9 figure figure NOUN cana-5313 108 10 2 2 NUM cana-5313 108 11 . . PUNCT cana-5313 109 1 the the DET cana-5313 109 2 bayesian bayesian NOUN cana-5313 109 3 approach approach NOUN cana-5313 109 4 demonstrated demonstrate VERB cana-5313 109 5 superior superior ADJ cana-5313 109 6 performance performance NOUN cana-5313 109 7 than than ADP cana-5313 109 8 the the DET cana-5313 109 9 traditional traditional ADJ cana-5313 109 10 model model NOUN cana-5313 109 11 by by ADP cana-5313 109 12 achieving achieve VERB cana-5313 109 13 5 5 NUM cana-5313 109 14 % % NOUN cana-5313 109 15 higher high ADJ cana-5313 109 16 accuracy accuracy NOUN cana-5313 109 17 levels level NOUN cana-5313 109 18 because because SCONJ cana-5313 109 19 it it PRON cana-5313 109 20 employs employ VERB cana-5313 109 21 prior prior ADJ cana-5313 109 22 information information NOUN cana-5313 109 23 during during ADP cana-5313 109 24 the the DET cana-5313 109 25 learning learning NOUN cana-5313 109 26 procedure procedure NOUN cana-5313 109 27 . . PUNCT cana-5313 110 1 figure figure NOUN cana-5313 110 2 2 2 NUM cana-5313 110 3 : : PUNCT cana-5313 110 4 accuracy accuracy NOUN cana-5313 110 5 comparison comparison NOUN cana-5313 110 6 between between ADP cana-5313 110 7 mle mle PROPN cana-5313 110 8 , , PUNCT cana-5313 110 9 bayesian bayesian NOUN cana-5313 110 10 , , PUNCT cana-5313 110 11 and and CCONJ cana-5313 110 12 traditional traditional ADJ cana-5313 110 13 logistic logistic ADJ cana-5313 110 14 regression regression NOUN cana-5313 110 15 models model NOUN cana-5313 110 16 evaluation evaluation NOUN cana-5313 110 17 of of ADP cana-5313 110 18 model model NOUN cana-5313 110 19 prediction prediction NOUN cana-5313 110 20 uncertainty uncertainty NOUN cana-5313 110 21 occurred occur VERB cana-5313 110 22 through through ADP cana-5313 110 23 the the DET cana-5313 110 24 bayesian bayesian NOUN cana-5313 110 25 approach approach NOUN cana-5313 110 26 . . PUNCT cana-5313 111 1 the the DET cana-5313 111 2 algorithm algorithm NOUN cana-5313 111 3 calculated calculate VERB cana-5313 111 4 predictive predictive ADJ cana-5313 111 5 credible credible ADJ cana-5313 111 6 intervals interval NOUN cana-5313 111 7 through through ADP cana-5313 111 8 bayesian bayesian NOUN cana-5313 111 9 parameter parameter NOUN cana-5313 111 10 estimation estimation NOUN cana-5313 111 11 techniques technique NOUN cana-5313 111 12 . . PUNCT cana-5313 112 1 uncertainty uncertainty NOUN cana-5313 112 2 quantification quantification NOUN cana-5313 112 3 methods method NOUN cana-5313 112 4 generate generate VERB cana-5313 112 5 important important ADJ cana-5313 112 6 predictive predictive ADJ cana-5313 112 7 reliability reliability NOUN cana-5313 112 8 data datum NOUN cana-5313 112 9 during during ADP cana-5313 112 10 situations situation NOUN cana-5313 112 11 with with ADP cana-5313 112 12 noisy noisy ADJ cana-5313 112 13 or or CCONJ cana-5313 112 14 scarce scarce ADJ cana-5313 112 15 data datum NOUN cana-5313 112 16 . . PUNCT cana-5313 113 1 the the DET cana-5313 113 2 prediction prediction NOUN cana-5313 113 3 error error NOUN cana-5313 113 4 estimates estimate NOUN cana-5313 113 5 produced produce VERB cana-5313 113 6 by by ADP cana-5313 113 7 the the DET cana-5313 113 8 bayesian bayesian NOUN cana-5313 113 9 model model NOUN cana-5313 113 10 compare compare VERB cana-5313 113 11 with with ADP cana-5313 113 12 traditional traditional ADJ cana-5313 113 13 methods method NOUN cana-5313 113 14 through through ADP cana-5313 113 15 the the DET cana-5313 113 16 information information NOUN cana-5313 113 17 presented present VERB cana-5313 113 18 in in ADP cana-5313 113 19 table table NOUN cana-5313 113 20 1 1 NUM cana-5313 113 21 . . PUNCT cana-5313 114 1 through through ADP cana-5313 114 2 this this DET cana-5313 114 3 table table NOUN cana-5313 114 4 we we PRON cana-5313 114 5 learn learn VERB cana-5313 114 6 how how SCONJ cana-5313 114 7 the the DET cana-5313 114 8 bayesian bayesian NOUN cana-5313 114 9 model model NOUN cana-5313 114 10 improves improve VERB cana-5313 114 11 both both DET cana-5313 114 12 prediction prediction NOUN cana-5313 114 13 accuracy accuracy NOUN cana-5313 114 14 levels level NOUN cana-5313 114 15 and and CCONJ cana-5313 114 16 delivers deliver VERB cana-5313 114 17 a a DET cana-5313 114 18 detailed detailed ADJ cana-5313 114 19 uncertainty uncertainty NOUN cana-5313 114 20 analysis analysis NOUN cana-5313 114 21 for for ADP cana-5313 114 22 model model NOUN cana-5313 114 23 output output NOUN cana-5313 114 24 which which PRON cana-5313 114 25 proves prove VERB cana-5313 114 26 essential essential ADJ cana-5313 114 27 for for ADP cana-5313 114 28 high high ADJ cana-5313 114 29 - - PUNCT cana-5313 114 30 risk risk NOUN cana-5313 114 31 decision decision NOUN cana-5313 114 32 - - PUNCT cana-5313 114 33 making make VERB cana-5313 114 34 contexts context NOUN cana-5313 114 35 . . PUNCT cana-5313 115 1 table table NOUN cana-5313 115 2 1 1 NUM cana-5313 115 3 : : PUNCT cana-5313 115 4 comparison comparison NOUN cana-5313 115 5 of of ADP cana-5313 115 6 prediction prediction NOUN cana-5313 115 7 uncertainty uncertainty NOUN cana-5313 115 8 between between ADP cana-5313 115 9 bayesian bayesian NOUN cana-5313 115 10 and and CCONJ cana-5313 115 11 traditional traditional ADJ cana-5313 115 12 models model NOUN cana-5313 115 13 model model NOUN cana-5313 115 14 mean mean NOUN cana-5313 115 15 prediction prediction NOUN cana-5313 115 16 prediction prediction NOUN cana-5313 115 17 interval interval NOUN cana-5313 115 18 ( ( PUNCT cana-5313 115 19 95 95 NUM cana-5313 115 20 % % NOUN cana-5313 115 21 ) ) PUNCT cana-5313 115 22 bayesian bayesian NOUN cana-5313 115 23 0.89 0.89 NUM cana-5313 116 1 [ [ X cana-5313 116 2 0.80 0.80 NUM cana-5313 116 3 , , PUNCT cana-5313 116 4 0.98 0.98 NUM cana-5313 116 5 ] ] PUNCT cana-5313 116 6 traditional traditional ADJ cana-5313 116 7 0.85 0.85 NUM cana-5313 116 8 n n CCONJ cana-5313 116 9 / / SYM cana-5313 116 10 a a DET cana-5313 116 11 researchers researcher NOUN cana-5313 116 12 used use VERB cana-5313 116 13 these these DET cana-5313 116 14 methodologies methodology NOUN cana-5313 116 15 for for ADP cana-5313 116 16 regression regression NOUN cana-5313 116 17 problem problem NOUN cana-5313 116 18 analysis analysis NOUN cana-5313 116 19 during during ADP cana-5313 116 20 their their PRON cana-5313 116 21 second second ADJ cana-5313 116 22 experiment experiment NOUN cana-5313 116 23 . . PUNCT cana-5313 117 1 the the DET cana-5313 117 2 dataset dataset NOUN cana-5313 117 3 included include VERB cana-5313 117 4 50,000 50,000 NUM cana-5313 117 5 points point NOUN cana-5313 117 6 whose whose DET cana-5313 117 7 target target NOUN cana-5313 117 8 variable variable NOUN cana-5313 117 9 exhibited exhibit VERB cana-5313 117 10 continuous continuous ADJ cana-5313 117 11 behavior behavior NOUN cana-5313 117 12 . . PUNCT cana-5313 118 1 the the DET cana-5313 118 2 research research NOUN cana-5313 118 3 included include VERB cana-5313 118 4 model model NOUN cana-5313 118 5 training training NOUN cana-5313 118 6 and and CCONJ cana-5313 118 7 testing testing NOUN cana-5313 118 8 of of ADP cana-5313 118 9 both both DET cana-5313 118 10 mle mle NOUN cana-5313 118 11 as as ADV cana-5313 118 12 well well ADV cana-5313 118 13 as as ADP cana-5313 118 14 bayesian bayesian NOUN cana-5313 118 15 regression regression NOUN cana-5313 118 16 models model NOUN cana-5313 118 17 . . PUNCT cana-5313 119 1 the the DET cana-5313 119 2 bayesian bayesian NOUN cana-5313 119 3 regression regression NOUN cana-5313 119 4 model model NOUN cana-5313 119 5 produced produce VERB cana-5313 119 6 distributions distribution NOUN cana-5313 119 7 of of ADP cana-5313 119 8 estimated estimate VERB cana-5313 119 9 regression regression NOUN cana-5313 119 10 coefficients coefficient NOUN cana-5313 119 11 as as ADP cana-5313 119 12 posterior posterior ADJ cana-5313 119 13 distributions distribution NOUN cana-5313 119 14 so so SCONJ cana-5313 119 15 users user NOUN cana-5313 119 16 could could AUX cana-5313 119 17 better well ADV cana-5313 119 18 evaluate evaluate VERB cana-5313 119 19 model model NOUN cana-5313 119 20 parameter parameter NOUN cana-5313 119 21 91 91 NUM cana-5313 119 22 90 90 NUM cana-5313 119 23 89 89 NUM cana-5313 119 24 94 94 NUM cana-5313 119 25 92 92 NUM cana-5313 119 26 93 93 NUM cana-5313 119 27 89 89 NUM cana-5313 119 28 86 86 NUM cana-5313 119 29 87 87 NUM cana-5313 119 30 82 82 NUM cana-5313 119 31 84 84 NUM cana-5313 119 32 86 86 NUM cana-5313 119 33 88 88 NUM cana-5313 119 34 90 90 NUM cana-5313 119 35 92 92 NUM cana-5313 119 36 94 94 NUM cana-5313 119 37 96 96 NUM cana-5313 119 38 accuracy accuracy NOUN cana-5313 119 39 ( ( PUNCT cana-5313 119 40 % % INTJ cana-5313 119 41 ) ) PUNCT cana-5313 119 42 precision precision NOUN cana-5313 119 43 ( ( PUNCT cana-5313 119 44 % % INTJ cana-5313 119 45 ) ) PUNCT cana-5313 119 46 recall recall NOUN cana-5313 119 47 ( ( PUNCT cana-5313 119 48 % % NOUN cana-5313 119 49 ) ) PUNCT cana-5313 119 50 accuracy accuracy NOUN cana-5313 119 51 comparison comparison NOUN cana-5313 119 52 between between ADP cana-5313 119 53 mle mle PROPN cana-5313 119 54 , , PUNCT cana-5313 119 55 bayesian bayesian NOUN cana-5313 119 56 , , PUNCT cana-5313 119 57 and and CCONJ cana-5313 119 58 traditional traditional ADJ cana-5313 119 59 logistic logistic ADJ cana-5313 119 60 regression regression NOUN cana-5313 119 61 models model NOUN cana-5313 119 62 mle mle PROPN cana-5313 119 63 model model PROPN cana-5313 119 64 bayesian bayesian PROPN cana-5313 119 65 model model NOUN cana-5313 119 66 traditional traditional ADJ cana-5313 119 67 model model NOUN cana-5313 119 68 communications communication NOUN cana-5313 119 69 on on ADP cana-5313 119 70 applied apply VERB cana-5313 119 71 nonlinear nonlinear ADJ cana-5313 119 72 analysis analysis NOUN cana-5313 119 73 issn issn NOUN cana-5313 119 74 : : PUNCT cana-5313 119 75 1074 1074 NUM cana-5313 119 76 - - PUNCT cana-5313 119 77 133x 133x NUM cana-5313 119 78 vol vol NOUN cana-5313 119 79 31 31 NUM cana-5313 119 80 no no NOUN cana-5313 119 81 . . PUNCT cana-5313 120 1 8s 8s PROPN cana-5313 120 2 ( ( PUNCT cana-5313 120 3 2024 2024 NUM cana-5313 120 4 ) ) PUNCT cana-5313 120 5 942 942 NUM cana-5313 120 6 https://internationalpubls.com https://internationalpubls.com X cana-5313 120 7 uncertainty uncertainty NOUN cana-5313 120 8 . . PUNCT cana-5313 121 1 the the DET cana-5313 121 2 mle mle PROPN cana-5313 121 3 model model NOUN cana-5313 121 4 generated generate VERB cana-5313 121 5 single single ADJ cana-5313 121 6 - - PUNCT cana-5313 121 7 point point NOUN cana-5313 121 8 values value NOUN cana-5313 121 9 to to PART cana-5313 121 10 represent represent VERB cana-5313 121 11 its its PRON cana-5313 121 12 parameter parameter NOUN cana-5313 121 13 values value NOUN cana-5313 121 14 . . PUNCT cana-5313 122 1 the the DET cana-5313 122 2 figure figure NOUN cana-5313 122 3 displays display VERB cana-5313 122 4 root root NOUN cana-5313 122 5 mean mean VERB cana-5313 122 6 squared square VERB cana-5313 122 7 error error NOUN cana-5313 122 8 ( ( PUNCT cana-5313 122 9 rmse rmse NOUN cana-5313 122 10 ) ) PUNCT cana-5313 122 11 comparisons comparison NOUN cana-5313 122 12 between between ADP cana-5313 122 13 both both DET cana-5313 122 14 models model NOUN cana-5313 122 15 in in ADP cana-5313 122 16 figure figure NOUN cana-5313 122 17 3 3 NUM cana-5313 122 18 . . PUNCT cana-5313 123 1 the the DET cana-5313 123 2 bayesian bayesian NOUN cana-5313 123 3 regression regression NOUN cana-5313 123 4 model model NOUN cana-5313 123 5 outperformed outperform VERB cana-5313 123 6 other other ADJ cana-5313 123 7 models model NOUN cana-5313 123 8 by by ADP cana-5313 123 9 obtaining obtain VERB cana-5313 123 10 lower low ADJ cana-5313 123 11 rmse rmse NOUN cana-5313 123 12 suggesting suggest VERB cana-5313 123 13 its its PRON cana-5313 123 14 strong strong ADJ cana-5313 123 15 data data NOUN cana-5313 123 16 - - PUNCT cana-5313 123 17 fit fit NOUN cana-5313 123 18 capability capability NOUN cana-5313 123 19 and and CCONJ cana-5313 123 20 competence competence NOUN cana-5313 123 21 in in ADP cana-5313 123 22 assessing assess VERB cana-5313 123 23 estimation estimation NOUN cana-5313 123 24 uncertainty uncertainty NOUN cana-5313 123 25 . . PUNCT cana-5313 124 1 figure figure VERB cana-5313 124 2 3 3 NUM cana-5313 124 3 : : PUNCT cana-5313 124 4 rmse rmse ADJ cana-5313 124 5 comparison comparison NOUN cana-5313 124 6 between between ADP cana-5313 124 7 bayesian bayesian NOUN cana-5313 124 8 and and CCONJ cana-5313 124 9 mle mle PROPN cana-5313 124 10 regression regression NOUN cana-5313 124 11 models model VERB cana-5313 124 12 the the DET cana-5313 124 13 analysis analysis NOUN cana-5313 124 14 through through ADP cana-5313 124 15 frequentist frequentist NOUN cana-5313 124 16 methods method NOUN cana-5313 124 17 generated generate VERB cana-5313 124 18 useful useful ADJ cana-5313 124 19 information information NOUN cana-5313 124 20 during during ADP cana-5313 124 21 both both DET cana-5313 124 22 classification classification NOUN cana-5313 124 23 analysis analysis NOUN cana-5313 124 24 and and CCONJ cana-5313 124 25 the the DET cana-5313 124 26 regression regression NOUN cana-5313 124 27 process process NOUN cana-5313 124 28 . . PUNCT cana-5313 125 1 the the DET cana-5313 125 2 tests test NOUN cana-5313 125 3 of of ADP cana-5313 125 4 hypothesis hypothesis NOUN cana-5313 125 5 and and CCONJ cana-5313 125 6 confidence confidence NOUN cana-5313 125 7 intervals interval NOUN cana-5313 125 8 demonstrated demonstrate VERB cana-5313 125 9 how how SCONJ cana-5313 125 10 to to PART cana-5313 125 11 evaluate evaluate VERB cana-5313 125 12 statistical statistical ADJ cana-5313 125 13 importance importance NOUN cana-5313 125 14 for for ADP cana-5313 125 15 model model NOUN cana-5313 125 16 parameter parameter NOUN cana-5313 125 17 estimates estimate NOUN cana-5313 125 18 . . PUNCT cana-5313 126 1 the the DET cana-5313 126 2 p p NOUN cana-5313 126 3 - - PUNCT cana-5313 126 4 values value NOUN cana-5313 126 5 analysis analysis NOUN cana-5313 126 6 in in ADP cana-5313 126 7 the the DET cana-5313 126 8 logistic logistic ADJ cana-5313 126 9 regression regression NOUN cana-5313 126 10 experiment experiment NOUN cana-5313 126 11 through through ADP cana-5313 126 12 frequentist frequentist NOUN cana-5313 126 13 methods method NOUN cana-5313 126 14 showed show VERB cana-5313 126 15 that that SCONJ cana-5313 126 16 several several ADJ cana-5313 126 17 unconsidered unconsidered ADJ cana-5313 126 18 features feature NOUN cana-5313 126 19 became become VERB cana-5313 126 20 statistically statistically ADV cana-5313 126 21 significant significant ADJ cana-5313 126 22 during during ADP cana-5313 126 23 model model NOUN cana-5313 126 24 parameter parameter NOUN cana-5313 126 25 tests test NOUN cana-5313 126 26 . . PUNCT cana-5313 127 1 the the DET cana-5313 127 2 use use NOUN cana-5313 127 3 of of ADP cana-5313 127 4 statistical statistical ADJ cana-5313 127 5 inference inference NOUN cana-5313 127 6 methods method NOUN cana-5313 127 7 achieves achieve VERB cana-5313 127 8 stronger strong ADJ cana-5313 127 9 standards standard NOUN cana-5313 127 10 when when SCONJ cana-5313 127 11 evaluating evaluate VERB cana-5313 127 12 model model NOUN cana-5313 127 13 parameters parameter NOUN cana-5313 127 14 in in ADP cana-5313 127 15 their their PRON cana-5313 127 16 connection connection NOUN cana-5313 127 17 to to ADP cana-5313 127 18 the the DET cana-5313 127 19 target target NOUN cana-5313 127 20 variable variable NOUN cana-5313 127 21 . . PUNCT cana-5313 128 1 the the DET cana-5313 128 2 main main ADJ cana-5313 128 3 parameters parameter NOUN cana-5313 128 4 from from ADP cana-5313 128 5 logistic logistic ADJ cana-5313 128 6 regression regression NOUN cana-5313 128 7 models model NOUN cana-5313 128 8 based base VERB cana-5313 128 9 on on ADP cana-5313 128 10 mle mle PROPN cana-5313 128 11 and and CCONJ cana-5313 128 12 bayesian bayesian NOUN cana-5313 128 13 inference inference NOUN cana-5313 128 14 and and CCONJ cana-5313 128 15 traditional traditional ADJ cana-5313 128 16 training training NOUN cana-5313 128 17 distribute distribute VERB cana-5313 128 18 across across ADP cana-5313 128 19 table table NOUN cana-5313 128 20 2 2 NUM cana-5313 128 21 for for ADP cana-5313 128 22 comparison comparison NOUN cana-5313 128 23 . . PUNCT cana-5313 129 1 accuracy accuracy NOUN cana-5313 129 2 together together ADV cana-5313 129 3 with with ADP cana-5313 129 4 precision precision NOUN cana-5313 129 5 , , PUNCT cana-5313 129 6 recall recall NOUN cana-5313 129 7 , , PUNCT cana-5313 129 8 f1 f1 NOUN cana-5313 129 9 - - PUNCT cana-5313 129 10 score score NOUN cana-5313 129 11 and and CCONJ cana-5313 129 12 p p NOUN cana-5313 129 13 - - PUNCT cana-5313 129 14 values value NOUN cana-5313 129 15 for for ADP cana-5313 129 16 coefficients coefficient NOUN cana-5313 129 17 represent represent VERB cana-5313 129 18 the the DET cana-5313 129 19 examined examine VERB cana-5313 129 20 metrics metric NOUN cana-5313 129 21 . . PUNCT cana-5313 130 1 table table NOUN cana-5313 130 2 2 2 NUM cana-5313 130 3 : : PUNCT cana-5313 130 4 comparison comparison NOUN cana-5313 130 5 of of ADP cana-5313 130 6 key key ADJ cana-5313 130 7 metrics metric NOUN cana-5313 130 8 for for ADP cana-5313 130 9 mle mle NOUN cana-5313 130 10 , , PUNCT cana-5313 130 11 bayesian bayesian NOUN cana-5313 130 12 , , PUNCT cana-5313 130 13 and and CCONJ cana-5313 130 14 traditional traditional ADJ cana-5313 130 15 logistic logistic ADJ cana-5313 130 16 regression regression NOUN cana-5313 130 17 models model NOUN cana-5313 130 18 metric metric ADJ cana-5313 130 19 mle mle PROPN cana-5313 130 20 model model NOUN cana-5313 130 21 bayesian bayesian PROPN cana-5313 130 22 model model NOUN cana-5313 130 23 traditional traditional ADJ cana-5313 130 24 model model NOUN cana-5313 130 25 accuracy accuracy NOUN cana-5313 130 26 0.91 0.91 NUM cana-5313 130 27 0.94 0.94 NUM cana-5313 130 28 0.89 0.89 NUM cana-5313 130 29 precision precision NOUN cana-5313 130 30 0.90 0.90 NUM cana-5313 130 31 0.92 0.92 NUM cana-5313 130 32 0.86 0.86 NUM cana-5313 130 33 recall recall NOUN cana-5313 130 34 0.89 0.89 NUM cana-5313 130 35 0.93 0.93 NUM cana-5313 130 36 0.87 0.87 NUM cana-5313 130 37 f1 f1 NOUN cana-5313 130 38 - - PUNCT cana-5313 130 39 score score NOUN cana-5313 130 40 0.89 0.89 NUM cana-5313 130 41 0.92 0.92 NUM cana-5313 130 42 0.86 0.86 NUM cana-5313 130 43 p p ADJ cana-5313 130 44 - - PUNCT cana-5313 130 45 value value NOUN cana-5313 130 46 ( ( PUNCT cana-5313 130 47 coeffs coeff NOUN cana-5313 130 48 ) ) PUNCT cana-5313 130 49 0.05 0.05 NUM cana-5313 130 50 0.03 0.03 NUM cana-5313 130 51 0.08 0.08 NUM cana-5313 130 52 this this DET cana-5313 130 53 methodology methodology NOUN cana-5313 130 54 enables enable VERB cana-5313 130 55 handling handle VERB cana-5313 130 56 situations situation NOUN cana-5313 130 57 when when SCONJ cana-5313 130 58 deep deep ADJ cana-5313 130 59 learning learning NOUN cana-5313 130 60 models model NOUN cana-5313 130 61 encounter encounter VERB cana-5313 130 62 uncertainty uncertainty NOUN cana-5313 130 63 during during ADP cana-5313 130 64 operation operation NOUN cana-5313 130 65 . . PUNCT cana-5313 131 1 during during ADP cana-5313 131 2 deep deep ADJ cana-5313 131 3 learning learning NOUN cana-5313 131 4 experiments experiment NOUN cana-5313 131 5 we we PRON cana-5313 131 6 applied apply VERB cana-5313 131 7 monte monte PROPN cana-5313 131 8 carlo carlo PROPN cana-5313 131 9 0.25 0.25 NUM cana-5313 131 10 0.3 0.3 NUM cana-5313 131 11 0.35 0.35 NUM cana-5313 131 12 0.28 0.28 NUM cana-5313 131 13 0.33 0.33 NUM cana-5313 131 14 0.38 0.38 NUM cana-5313 131 15 0.92 0.92 NUM cana-5313 131 16 0.89 0.89 NUM cana-5313 131 17 0.85 0.85 NUM cana-5313 131 18 0 0 NUM cana-5313 131 19 0.1 0.1 NUM cana-5313 131 20 0.2 0.2 NUM cana-5313 131 21 0.3 0.3 NUM cana-5313 131 22 0.4 0.4 NUM cana-5313 131 23 0.5 0.5 NUM cana-5313 131 24 0.6 0.6 NUM cana-5313 131 25 0.7 0.7 NUM cana-5313 131 26 0.8 0.8 NUM cana-5313 131 27 0.9 0.9 NUM cana-5313 131 28 1 1 NUM cana-5313 131 29 bayesian bayesian NOUN cana-5313 131 30 model model NOUN cana-5313 131 31 mle mle PROPN cana-5313 131 32 model model PROPN cana-5313 131 33 traditional traditional PROPN cana-5313 131 34 model model PROPN cana-5313 131 35 rmse rmse PROPN cana-5313 131 36 comparison comparison NOUN cana-5313 131 37 between between ADP cana-5313 131 38 bayesian bayesian NOUN cana-5313 131 39 and and CCONJ cana-5313 131 40 mle mle PROPN cana-5313 131 41 regression regression NOUN cana-5313 131 42 models model NOUN cana-5313 131 43 rΒ² rΒ² VERB cana-5313 131 44 ( ( PUNCT cana-5313 131 45 testing testing NOUN cana-5313 131 46 ) ) PUNCT cana-5313 131 47 rmse rmse NOUN cana-5313 131 48 ( ( PUNCT cana-5313 131 49 testing testing NOUN cana-5313 131 50 ) ) PUNCT cana-5313 131 51 rmse rmse NOUN cana-5313 131 52 ( ( PUNCT cana-5313 131 53 training training NOUN cana-5313 131 54 ) ) PUNCT cana-5313 131 55 communications communication NOUN cana-5313 131 56 on on ADP cana-5313 131 57 applied apply VERB cana-5313 131 58 nonlinear nonlinear ADJ cana-5313 131 59 analysis analysis NOUN cana-5313 131 60 issn issn NOUN cana-5313 131 61 : : PUNCT cana-5313 131 62 1074 1074 NUM cana-5313 131 63 - - PUNCT cana-5313 131 64 133x 133x NUM cana-5313 131 65 vol vol NOUN cana-5313 131 66 31 31 NUM cana-5313 131 67 no no NOUN cana-5313 131 68 . . PUNCT cana-5313 132 1 8s 8s PROPN cana-5313 132 2 ( ( PUNCT cana-5313 132 3 2024 2024 NUM cana-5313 132 4 ) ) PUNCT cana-5313 132 5 943 943 NUM cana-5313 132 6 https://internationalpubls.com https://internationalpubls.com X cana-5313 132 7 dropout dropout NOUN cana-5313 132 8 for for ADP cana-5313 132 9 estimating estimate VERB cana-5313 132 10 neural neural ADJ cana-5313 132 11 network network NOUN cana-5313 132 12 prediction prediction NOUN cana-5313 132 13 uncertainties uncertainty NOUN cana-5313 132 14 . . PUNCT cana-5313 133 1 the the DET cana-5313 133 2 simulation simulation NOUN cana-5313 133 3 of of ADP cana-5313 133 4 bayesian bayesian NOUN cana-5313 133 5 posterior posterior NOUN cana-5313 133 6 was be AUX cana-5313 133 7 done do VERB cana-5313 133 8 through through ADP cana-5313 133 9 inference inference NOUN cana-5313 133 10 dropout dropout NOUN cana-5313 133 11 application application NOUN cana-5313 133 12 for for ADP cana-5313 133 13 model model NOUN cana-5313 133 14 comparison comparison NOUN cana-5313 133 15 with with ADP cana-5313 133 16 standard standard ADJ cana-5313 133 17 deterministic deterministic ADJ cana-5313 133 18 neural neural ADJ cana-5313 133 19 networks network NOUN cana-5313 133 20 . . PUNCT cana-5313 134 1 the the DET cana-5313 134 2 results result NOUN cana-5313 134 3 indicate indicate VERB cana-5313 134 4 that that SCONJ cana-5313 134 5 monte monte PROPN cana-5313 134 6 carlo carlo PROPN cana-5313 134 7 dropout dropout PROPN cana-5313 134 8 proves prove VERB cana-5313 134 9 essential essential ADJ cana-5313 134 10 in in ADP cana-5313 134 11 prediction prediction NOUN cana-5313 134 12 uncertainty uncertainty NOUN cana-5313 134 13 estimation estimation NOUN cana-5313 134 14 as as SCONJ cana-5313 134 15 it it PRON cana-5313 134 16 enables enable VERB cana-5313 134 17 crucial crucial ADJ cana-5313 134 18 clinical clinical ADJ cana-5313 134 19 decision decision NOUN cana-5313 134 20 making make VERB cana-5313 134 21 for for ADP cana-5313 134 22 medical medical ADJ cana-5313 134 23 diagnoses diagnosis NOUN cana-5313 134 24 . . PUNCT cana-5313 135 1 bayesian bayesian NOUN cana-5313 135 2 methods method NOUN cana-5313 135 3 supply supply VERB cana-5313 135 4 a a DET cana-5313 135 5 detailed detailed ADJ cana-5313 135 6 understanding understanding NOUN cana-5313 135 7 of of ADP cana-5313 135 8 model model NOUN cana-5313 135 9 processing process VERB cana-5313 135 10 through through ADP cana-5313 135 11 both both PRON cana-5313 135 12 statistical statistical ADJ cana-5313 135 13 knowledge knowledge NOUN cana-5313 135 14 application application NOUN cana-5313 135 15 and and CCONJ cana-5313 135 16 uncertainty uncertainty NOUN cana-5313 135 17 estimation estimation NOUN cana-5313 135 18 . . PUNCT cana-5313 136 1 this this DET cana-5313 136 2 method method NOUN cana-5313 136 3 returns return VERB cana-5313 136 4 valuable valuable ADJ cana-5313 136 5 results result NOUN cana-5313 136 6 when when SCONJ cana-5313 136 7 dealing deal VERB cana-5313 136 8 with with ADP cana-5313 136 9 limited limited ADJ cana-5313 136 10 or or CCONJ cana-5313 136 11 volatile volatile ADJ cana-5313 136 12 datasets dataset NOUN cana-5313 136 13 since since SCONJ cana-5313 136 14 it it PRON cana-5313 136 15 works work VERB cana-5313 136 16 well well ADV cana-5313 136 17 for for ADP cana-5313 136 18 healthcare healthcare NOUN cana-5313 136 19 and and CCONJ cana-5313 136 20 financial financial ADJ cana-5313 136 21 applications application NOUN cana-5313 136 22 . . PUNCT cana-5313 137 1 neither neither CCONJ cana-5313 137 2 frequentist frequentist NOUN cana-5313 137 3 methods method NOUN cana-5313 137 4 fulfill fulfill VERB cana-5313 137 5 hypothesis hypothesis NOUN cana-5313 137 6 testing testing NOUN cana-5313 137 7 nor nor CCONJ cana-5313 137 8 parameter parameter NOUN cana-5313 137 9 estimation estimation NOUN cana-5313 137 10 but but CCONJ cana-5313 137 11 they they PRON cana-5313 137 12 demonstrate demonstrate VERB cana-5313 137 13 limited limited ADJ cana-5313 137 14 capability capability NOUN cana-5313 137 15 for for ADP cana-5313 137 16 uncertainty uncertainty NOUN cana-5313 137 17 evaluation evaluation NOUN cana-5313 137 18 . . PUNCT cana-5313 138 1 proposing propose VERB cana-5313 138 2 point point NOUN cana-5313 138 3 estimates estimate NOUN cana-5313 138 4 is be AUX cana-5313 138 5 mle mle PROPN cana-5313 138 6 's 's PART cana-5313 138 7 strongest strong ADJ cana-5313 138 8 capability capability NOUN cana-5313 138 9 yet yet CCONJ cana-5313 138 10 the the DET cana-5313 138 11 method method NOUN cana-5313 138 12 fails fail VERB cana-5313 138 13 to to PART cana-5313 138 14 reveal reveal VERB cana-5313 138 15 complete complete ADJ cana-5313 138 16 model model NOUN cana-5313 138 17 uncertainties uncertainty NOUN cana-5313 138 18 which which PRON cana-5313 138 19 prevents prevent VERB cana-5313 138 20 its its PRON cana-5313 138 21 valid valid ADJ cana-5313 138 22 use use NOUN cana-5313 138 23 at at ADP cana-5313 138 24 high high ADJ cana-5313 138 25 risk risk NOUN cana-5313 138 26 levels level NOUN cana-5313 138 27 . . PUNCT cana-5313 139 1 the the DET cana-5313 139 2 combination combination NOUN cana-5313 139 3 of of ADP cana-5313 139 4 statistical statistical ADJ cana-5313 139 5 inference inference NOUN cana-5313 139 6 with with ADP cana-5313 139 7 machine machine NOUN cana-5313 139 8 learning learning NOUN cana-5313 139 9 models model NOUN cana-5313 139 10 improves improve VERB cana-5313 139 11 the the DET cana-5313 139 12 assessment assessment NOUN cana-5313 139 13 as as ADV cana-5313 139 14 well well ADV cana-5313 139 15 as as ADP cana-5313 139 16 validation validation NOUN cana-5313 139 17 process process NOUN cana-5313 139 18 . . PUNCT cana-5313 140 1 the the DET cana-5313 140 2 research research NOUN cana-5313 140 3 utilized utilize VERB cana-5313 140 4 cross cross ADJ cana-5313 140 5 - - ADJ cana-5313 140 6 validation validation ADJ cana-5313 140 7 techniques technique NOUN cana-5313 140 8 throughout throughout ADP cana-5313 140 9 all all DET cana-5313 140 10 experiments experiment NOUN cana-5313 140 11 to to PART cana-5313 140 12 validate validate VERB cana-5313 140 13 model model NOUN cana-5313 140 14 performance performance NOUN cana-5313 140 15 when when SCONJ cana-5313 140 16 processing process VERB cana-5313 140 17 new new ADJ cana-5313 140 18 unseen unseen ADJ cana-5313 140 19 data datum NOUN cana-5313 140 20 . . PUNCT cana-5313 141 1 the the DET cana-5313 141 2 combination combination NOUN cana-5313 141 3 of of ADP cana-5313 141 4 statistical statistical ADJ cana-5313 141 5 inference inference NOUN cana-5313 141 6 approaches approach NOUN cana-5313 141 7 with with ADP cana-5313 141 8 bayesian bayesian NOUN cana-5313 141 9 methodology methodology NOUN cana-5313 141 10 produces produce VERB cana-5313 141 11 better well ADV cana-5313 141 12 generalizing generalize VERB cana-5313 141 13 outcome outcome NOUN cana-5313 141 14 since since SCONJ cana-5313 141 15 these these DET cana-5313 141 16 methods method NOUN cana-5313 141 17 reduce reduce VERB cana-5313 141 18 the the DET cana-5313 141 19 occurrence occurrence NOUN cana-5313 141 20 of of ADP cana-5313 141 21 training training NOUN cana-5313 141 22 data datum NOUN cana-5313 141 23 overfitting overfitting NOUN cana-5313 141 24 . . PUNCT cana-5313 142 1 such such ADJ cana-5313 142 2 methodology methodology NOUN cana-5313 142 3 brings bring VERB cana-5313 142 4 advantages advantage NOUN cana-5313 142 5 to to ADP cana-5313 142 6 complex complex ADJ cana-5313 142 7 datasets dataset NOUN cana-5313 142 8 by by ADP cana-5313 142 9 improving improve VERB cana-5313 142 10 the the DET cana-5313 142 11 modeling modeling NOUN cana-5313 142 12 process process NOUN cana-5313 142 13 when when SCONJ cana-5313 142 14 actual actual ADJ cana-5313 142 15 data datum NOUN cana-5313 142 16 does do AUX cana-5313 142 17 not not PART cana-5313 142 18 properly properly ADV cana-5313 142 19 reflect reflect VERB cana-5313 142 20 its its PRON cana-5313 142 21 underlying underlie VERB cana-5313 142 22 distribution distribution NOUN cana-5313 142 23 . . PUNCT cana-5313 143 1 statistical statistical ADJ cana-5313 143 2 inference inference NOUN cana-5313 143 3 methods method NOUN cana-5313 143 4 especially especially ADV cana-5313 143 5 bayesian bayesian VERB cana-5313 143 6 inference inference NOUN cana-5313 143 7 enhance enhance VERB cana-5313 143 8 machine machine NOUN cana-5313 143 9 learning learning NOUN cana-5313 143 10 model model NOUN cana-5313 143 11 performance performance NOUN cana-5313 143 12 by by ADP cana-5313 143 13 producing produce VERB cana-5313 143 14 superior superior ADJ cana-5313 143 15 results result NOUN cana-5313 143 16 since since SCONJ cana-5313 143 17 they they PRON cana-5313 143 18 add add VERB cana-5313 143 19 interpretation interpretation NOUN cana-5313 143 20 capabilities capability NOUN cana-5313 143 21 to to ADP cana-5313 143 22 models model NOUN cana-5313 143 23 . . PUNCT cana-5313 144 1 the the DET cana-5313 144 2 method method NOUN cana-5313 144 3 proves prove VERB cana-5313 144 4 especially especially ADV cana-5313 144 5 beneficial beneficial ADJ cana-5313 144 6 when when SCONJ cana-5313 144 7 used use VERB cana-5313 144 8 in in ADP cana-5313 144 9 healthcare healthcare NOUN cana-5313 144 10 combined combine VERB cana-5313 144 11 with with ADP cana-5313 144 12 finance finance NOUN cana-5313 144 13 and and CCONJ cana-5313 144 14 autonomous autonomous ADJ cana-5313 144 15 systems system NOUN cana-5313 144 16 because because SCONJ cana-5313 144 17 uncertainty uncertainty NOUN cana-5313 144 18 and and CCONJ cana-5313 144 19 decision decision NOUN cana-5313 144 20 - - PUNCT cana-5313 144 21 making make VERB cana-5313 144 22 control control NOUN cana-5313 144 23 successful successful ADJ cana-5313 144 24 conclusions conclusion NOUN cana-5313 144 25 . . PUNCT cana-5313 145 1 v. v. ADP cana-5313 145 2 conclusion conclusion NOUN cana-5313 145 3 statistical statistical ADJ cana-5313 145 4 inference inference NOUN cana-5313 145 5 offers offer VERB cana-5313 145 6 organizations organization NOUN cana-5313 145 7 a a DET cana-5313 145 8 strong strong ADJ cana-5313 145 9 method method NOUN cana-5313 145 10 to to PART cana-5313 145 11 understand understand VERB cana-5313 145 12 and and CCONJ cana-5313 145 13 interpret interpret VERB cana-5313 145 14 the the DET cana-5313 145 15 workings working NOUN cana-5313 145 16 of of ADP cana-5313 145 17 machine machine NOUN cana-5313 145 18 learning learning NOUN cana-5313 145 19 models model NOUN cana-5313 145 20 . . PUNCT cana-5313 146 1 statistical statistical ADJ cana-5313 146 2 inference inference NOUN cana-5313 146 3 includes include VERB cana-5313 146 4 mle mle PROPN cana-5313 146 5 and and CCONJ cana-5313 146 6 bayesian bayesian NOUN cana-5313 146 7 inference inference NOUN cana-5313 146 8 and and CCONJ cana-5313 146 9 frequentist frequentist NOUN cana-5313 146 10 inference inference NOUN cana-5313 146 11 as as ADP cana-5313 146 12 its its PRON cana-5313 146 13 main main ADJ cana-5313 146 14 methodologies methodology NOUN cana-5313 146 15 which which PRON cana-5313 146 16 are be AUX cana-5313 146 17 applied apply VERB cana-5313 146 18 throughout throughout ADP cana-5313 146 19 machine machine NOUN cana-5313 146 20 learning learning NOUN cana-5313 146 21 systems system NOUN cana-5313 146 22 . . PUNCT cana-5313 147 1 machine machine NOUN cana-5313 147 2 learning learn VERB cana-5313 147 3 algorithms algorithm NOUN cana-5313 147 4 become become VERB cana-5313 147 5 both both PRON cana-5313 147 6 reliable reliable ADJ cana-5313 147 7 for for ADP cana-5313 147 8 predictions prediction NOUN cana-5313 147 9 and and CCONJ cana-5313 147 10 maintain maintain VERB cana-5313 147 11 interpretability interpretability NOUN cana-5313 147 12 together together ADV cana-5313 147 13 with with ADP cana-5313 147 14 generalizable generalizable ADJ cana-5313 147 15 outcomes outcome NOUN cana-5313 147 16 through through ADP cana-5313 147 17 these these DET cana-5313 147 18 method method NOUN cana-5313 147 19 integrations integration NOUN cana-5313 147 20 . . PUNCT cana-5313 148 1 references reference NOUN cana-5313 148 2 [ [ X cana-5313 148 3 1 1 NUM cana-5313 148 4 ] ] PUNCT cana-5313 148 5 k. k. PROPN cana-5313 148 6 makar makar PROPN cana-5313 148 7 and and CCONJ cana-5313 148 8 a. a. PROPN cana-5313 148 9 rubin rubin PROPN cana-5313 148 10 , , PUNCT cana-5313 148 11 β€œ " PUNCT cana-5313 148 12 learning learn VERB cana-5313 148 13 about about ADP cana-5313 148 14 statistical statistical ADJ cana-5313 148 15 inference inference NOUN cana-5313 148 16 , , PUNCT cana-5313 148 17 ” " PUNCT cana-5313 148 18 in in ADP cana-5313 148 19 springer springer NOUN cana-5313 148 20 international international ADJ cana-5313 148 21 handbooks handbook NOUN cana-5313 148 22 of of ADP cana-5313 148 23 education education NOUN cana-5313 148 24 , , PUNCT cana-5313 148 25 2017 2017 NUM cana-5313 148 26 , , PUNCT cana-5313 148 27 pp pp ADJ cana-5313 148 28 . . PUNCT cana-5313 149 1 261–294 261–294 NUM cana-5313 149 2 . . PUNCT cana-5313 150 1 doi doi NOUN cana-5313 150 2 : : PUNCT cana-5313 150 3 10.1007/978 10.1007/978 NUM cana-5313 150 4 - - SYM cana-5313 150 5 3 3 NUM cana-5313 150 6 - - PUNCT cana-5313 150 7 31966195 31966195 NUM cana-5313 150 8 - - SYM cana-5313 150 9 7_8 7_8 NUM cana-5313 150 10 . . PUNCT cana-5313 151 1 [ [ X cana-5313 151 2 2 2 NUM cana-5313 151 3 ] ] PUNCT cana-5313 151 4 m. m. NOUN cana-5313 151 5 j. j. PROPN cana-5313 151 6 van van PROPN cana-5313 151 7 der der PROPN cana-5313 151 8 laan laan PROPN cana-5313 151 9 and and CCONJ cana-5313 151 10 r. r. PROPN cana-5313 151 11 j. j. PROPN cana-5313 151 12 c. c. PROPN cana-5313 151 13 m. m. PROPN cana-5313 151 14 starmans starmans PROPN cana-5313 151 15 , , PUNCT cana-5313 151 16 β€œ " PUNCT cana-5313 151 17 entering enter VERB cana-5313 151 18 the the DET cana-5313 151 19 era era NOUN cana-5313 151 20 of of ADP cana-5313 151 21 data datum NOUN cana-5313 151 22 science science NOUN cana-5313 151 23 : : PUNCT cana-5313 151 24 targeted target VERB cana-5313 151 25 learning learning NOUN cana-5313 151 26 and and CCONJ cana-5313 151 27 the the DET cana-5313 151 28 integration integration NOUN cana-5313 151 29 of of ADP cana-5313 151 30 statistics statistic NOUN cana-5313 151 31 and and CCONJ cana-5313 151 32 computational computational ADJ cana-5313 151 33 data datum NOUN cana-5313 151 34 analysis analysis NOUN cana-5313 151 35 , , PUNCT cana-5313 151 36 ” " PUNCT cana-5313 151 37 advances advance NOUN cana-5313 151 38 in in ADP cana-5313 151 39 statistics statistic NOUN cana-5313 151 40 , , PUNCT cana-5313 151 41 vol vol NOUN cana-5313 151 42 . . PROPN cana-5313 151 43 2014 2014 NUM cana-5313 151 44 , , PUNCT cana-5313 151 45 pp pp ADP cana-5313 151 46 . . PUNCT cana-5313 152 1 1–19 1–19 PROPN cana-5313 152 2 , , PUNCT cana-5313 152 3 sep sep PROPN cana-5313 152 4 . . PROPN cana-5313 152 5 2014 2014 NUM cana-5313 152 6 , , PUNCT cana-5313 152 7 doi doi NOUN cana-5313 152 8 : : PUNCT cana-5313 152 9 10.1155/2014/502678 10.1155/2014/502678 NUM cana-5313 152 10 . . PUNCT cana-5313 153 1 [ [ X cana-5313 153 2 3 3 NUM cana-5313 153 3 ] ] PUNCT cana-5313 153 4 a. a. NOUN cana-5313 153 5 spanos spanos PROPN cana-5313 153 6 , , PUNCT cana-5313 153 7 β€œ " PUNCT cana-5313 153 8 statistical statistical ADJ cana-5313 153 9 modeling modeling NOUN cana-5313 153 10 and and CCONJ cana-5313 153 11 inference inference NOUN cana-5313 153 12 in in ADP cana-5313 153 13 the the DET cana-5313 153 14 era era NOUN cana-5313 153 15 of of ADP cana-5313 153 16 data datum NOUN cana-5313 153 17 science science NOUN cana-5313 153 18 and and CCONJ cana-5313 153 19 graphical graphical ADJ cana-5313 153 20 causal causal NOUN cana-5313 153 21 modeling modeling NOUN cana-5313 153 22 , , PUNCT cana-5313 153 23 ” " PUNCT cana-5313 153 24 journal journal NOUN cana-5313 153 25 of of ADP cana-5313 153 26 economic economic ADJ cana-5313 153 27 surveys survey NOUN cana-5313 153 28 , , PUNCT cana-5313 153 29 vol vol NOUN cana-5313 153 30 . . PROPN cana-5313 153 31 36 36 NUM cana-5313 153 32 , , PUNCT cana-5313 153 33 no no INTJ cana-5313 153 34 . . NOUN cana-5313 153 35 5 5 NUM cana-5313 153 36 , , PUNCT cana-5313 153 37 pp pp ADJ cana-5313 153 38 . . PUNCT cana-5313 154 1 1251–1287 1251–1287 NUM cana-5313 154 2 , , PUNCT cana-5313 154 3 nov nov PROPN cana-5313 154 4 . . PROPN cana-5313 154 5 2021 2021 NUM cana-5313 154 6 , , PUNCT cana-5313 154 7 doi doi NOUN cana-5313 154 8 : : PUNCT cana-5313 154 9 10.1111 10.1111 NUM cana-5313 154 10 / / SYM cana-5313 154 11 joes.12483 joes.12483 PROPN cana-5313 154 12 . . PUNCT cana-5313 155 1 [ [ X cana-5313 155 2 4 4 X cana-5313 155 3 ] ] PUNCT cana-5313 155 4 z. z. PROPN cana-5313 155 5 yang yang PROPN cana-5313 155 6 , , PUNCT cana-5313 155 7 a. a. NOUN cana-5313 155 8 gang gang NOUN cana-5313 155 9 , , PUNCT cana-5313 155 10 and and CCONJ cana-5313 155 11 w. w. PROPN cana-5313 155 12 u. u. PROPN cana-5313 155 13 bajwa bajwa PROPN cana-5313 155 14 , , PUNCT cana-5313 155 15 β€œ " PUNCT cana-5313 155 16 adversary adversary NOUN cana-5313 155 17 - - PUNCT cana-5313 155 18 resilient resilient ADJ cana-5313 155 19 distributed distribute VERB cana-5313 155 20 and and CCONJ cana-5313 155 21 decentralized decentralize VERB cana-5313 155 22 statistical statistical ADJ cana-5313 155 23 inference inference NOUN cana-5313 155 24 and and CCONJ cana-5313 155 25 machine machine NOUN cana-5313 155 26 learning learning NOUN cana-5313 155 27 : : PUNCT cana-5313 155 28 an an DET cana-5313 155 29 overview overview NOUN cana-5313 155 30 of of ADP cana-5313 155 31 recent recent ADJ cana-5313 155 32 communications communication NOUN cana-5313 155 33 on on ADP cana-5313 155 34 applied apply VERB cana-5313 155 35 nonlinear nonlinear ADJ cana-5313 155 36 analysis analysis NOUN cana-5313 155 37 issn issn NOUN cana-5313 155 38 : : PUNCT cana-5313 155 39 1074 1074 NUM cana-5313 155 40 - - PUNCT cana-5313 155 41 133x 133x NUM cana-5313 155 42 vol vol NOUN cana-5313 155 43 31 31 NUM cana-5313 155 44 no no NOUN cana-5313 155 45 . . PUNCT cana-5313 156 1 8s 8s PROPN cana-5313 156 2 ( ( PUNCT cana-5313 156 3 2024 2024 NUM cana-5313 156 4 ) ) PUNCT cana-5313 156 5 944 944 NUM cana-5313 156 6 https://internationalpubls.com https://internationalpubls.com X cana-5313 156 7 advances advance VERB cana-5313 156 8 under under ADP cana-5313 156 9 the the DET cana-5313 156 10 byzantine byzantine ADJ cana-5313 156 11 threat threat NOUN cana-5313 156 12 model model NOUN cana-5313 156 13 , , PUNCT cana-5313 156 14 ” " PUNCT cana-5313 156 15 ieee ieee NOUN cana-5313 156 16 signal signal NOUN cana-5313 156 17 processing processing NOUN cana-5313 156 18 magazine magazine NOUN cana-5313 156 19 , , PUNCT cana-5313 156 20 vol vol NOUN cana-5313 156 21 . . PROPN cana-5313 157 1 37 37 NUM cana-5313 157 2 , , PUNCT cana-5313 157 3 no no INTJ cana-5313 157 4 . . NOUN cana-5313 157 5 3 3 NUM cana-5313 157 6 , , PUNCT cana-5313 157 7 pp pp ADJ cana-5313 157 8 . . PUNCT cana-5313 158 1 146–159 146–159 NUM cana-5313 158 2 , , PUNCT cana-5313 158 3 may may AUX cana-5313 158 4 2020 2020 NUM cana-5313 158 5 , , PUNCT cana-5313 158 6 doi doi NOUN cana-5313 158 7 : : PUNCT cana-5313 158 8 10.1109 10.1109 NUM cana-5313 158 9 / / SYM cana-5313 158 10 msp.2020.2973345 msp.2020.2973345 NOUN cana-5313 158 11 . . PUNCT cana-5313 159 1 [ [ X cana-5313 159 2 5 5 NUM cana-5313 159 3 ] ] PUNCT cana-5313 159 4 m. m. NOUN cana-5313 159 5 i. i. PROPN cana-5313 159 6 jordan jordan PROPN cana-5313 159 7 and and CCONJ cana-5313 159 8 t. t. PROPN cana-5313 159 9 m. m. PROPN cana-5313 159 10 mitchell mitchell PROPN cana-5313 159 11 , , PUNCT cana-5313 159 12 β€œ " PUNCT cana-5313 159 13 machine machine NOUN cana-5313 159 14 learning learning NOUN cana-5313 159 15 : : PUNCT cana-5313 159 16 trends trend NOUN cana-5313 159 17 , , PUNCT cana-5313 159 18 perspectives perspective NOUN cana-5313 159 19 , , PUNCT cana-5313 159 20 and and CCONJ cana-5313 159 21 prospects prospect NOUN cana-5313 159 22 , , PUNCT cana-5313 159 23 ” " PUNCT cana-5313 159 24 science science NOUN cana-5313 159 25 , , PUNCT cana-5313 159 26 vol vol NOUN cana-5313 159 27 . . PROPN cana-5313 159 28 349 349 NUM cana-5313 159 29 , , PUNCT cana-5313 159 30 no no INTJ cana-5313 159 31 . . NOUN cana-5313 159 32 6245 6245 NUM cana-5313 159 33 , , PUNCT cana-5313 159 34 pp pp X cana-5313 159 35 . . PUNCT cana-5313 160 1 255–260 255–260 NUM cana-5313 160 2 , , PUNCT cana-5313 160 3 jul jul PROPN cana-5313 160 4 . . PROPN cana-5313 160 5 2015 2015 NUM cana-5313 160 6 , , PUNCT cana-5313 160 7 doi doi NOUN cana-5313 160 8 : : PUNCT cana-5313 160 9 10.1126 10.1126 NUM cana-5313 160 10 / / SYM cana-5313 160 11 science.aaa8415 science.aaa8415 NUM cana-5313 160 12 . . PUNCT cana-5313 161 1 [ [ X cana-5313 161 2 6 6 X cana-5313 161 3 ] ] X cana-5313 161 4 y. y. PROPN cana-5313 161 5 liu liu PROPN cana-5313 161 6 et et PROPN cana-5313 161 7 al al PROPN cana-5313 161 8 . . PROPN cana-5313 161 9 , , PUNCT cana-5313 161 10 β€œ " PUNCT cana-5313 161 11 machine machine NOUN cana-5313 161 12 learning learning NOUN cana-5313 161 13 in in ADP cana-5313 161 14 materials material NOUN cana-5313 161 15 genome genome NOUN cana-5313 161 16 initiative initiative NOUN cana-5313 161 17 : : PUNCT cana-5313 161 18 a a DET cana-5313 161 19 review review NOUN cana-5313 161 20 , , PUNCT cana-5313 161 21 ” " PUNCT cana-5313 161 22 journal journal NOUN cana-5313 161 23 of of ADP cana-5313 161 24 material material NOUN cana-5313 161 25 science science NOUN cana-5313 161 26 and and CCONJ cana-5313 161 27 technology technology NOUN cana-5313 161 28 , , PUNCT cana-5313 161 29 vol vol NOUN cana-5313 161 30 . . PROPN cana-5313 161 31 57 57 NUM cana-5313 161 32 , , PUNCT cana-5313 161 33 pp pp ADJ cana-5313 161 34 . . PUNCT cana-5313 162 1 113–122 113–122 NUM cana-5313 162 2 , , PUNCT cana-5313 162 3 may may AUX cana-5313 162 4 2020 2020 NUM cana-5313 162 5 , , PUNCT cana-5313 162 6 doi doi NOUN cana-5313 162 7 : : PUNCT cana-5313 162 8 10.1016 10.1016 NUM cana-5313 162 9 / / SYM cana-5313 162 10 j.jmst.2020.01.067 j.jmst.2020.01.067 PROPN cana-5313 162 11 . . PUNCT cana-5313 163 1 [ [ X cana-5313 163 2 7 7 X cana-5313 163 3 ] ] PUNCT cana-5313 163 4 t. t. PROPN cana-5313 163 5 mou mou PROPN cana-5313 163 6 et et PROPN cana-5313 163 7 al al PROPN cana-5313 163 8 . . PROPN cana-5313 163 9 , , PUNCT cana-5313 163 10 β€œ " PUNCT cana-5313 163 11 bridging bridge VERB cana-5313 163 12 the the DET cana-5313 163 13 complexity complexity NOUN cana-5313 163 14 gap gap NOUN cana-5313 163 15 in in ADP cana-5313 163 16 computational computational ADJ cana-5313 163 17 heterogeneous heterogeneous ADJ cana-5313 163 18 catalysis catalysis NOUN cana-5313 163 19 with with ADP cana-5313 163 20 machine machine NOUN cana-5313 163 21 learning learning NOUN cana-5313 163 22 , , PUNCT cana-5313 163 23 ” " PUNCT cana-5313 163 24 nature nature NOUN cana-5313 163 25 catalysis catalysis NOUN cana-5313 163 26 , , PUNCT cana-5313 163 27 vol vol NOUN cana-5313 163 28 . . PROPN cana-5313 163 29 6 6 NUM cana-5313 163 30 , , PUNCT cana-5313 163 31 no no INTJ cana-5313 163 32 . . NOUN cana-5313 163 33 2 2 NUM cana-5313 163 34 , , PUNCT cana-5313 163 35 pp pp ADJ cana-5313 163 36 . . PUNCT cana-5313 164 1 122–136 122–136 NUM cana-5313 164 2 , , PUNCT cana-5313 164 3 feb feb PROPN cana-5313 164 4 . . PROPN cana-5313 164 5 2023 2023 NUM cana-5313 164 6 , , PUNCT cana-5313 164 7 doi doi NOUN cana-5313 164 8 : : PUNCT cana-5313 164 9 10.1038 10.1038 NUM cana-5313 164 10 / / SYM cana-5313 164 11 s41929 s41929 NOUN cana-5313 164 12 - - PUNCT cana-5313 164 13 023 023 NUM cana-5313 164 14 - - PUNCT cana-5313 164 15 00911 00911 NUM cana-5313 164 16 - - PUNCT cana-5313 164 17 w. w. NOUN cana-5313 165 1 [ [ X cana-5313 165 2 8 8 NUM cana-5313 165 3 ] ] PUNCT cana-5313 165 4 m. m. NOUN cana-5313 165 5 mowbray mowbray PROPN cana-5313 165 6 , , PUNCT cana-5313 165 7 m. m. NOUN cana-5313 165 8 vallerio vallerio PROPN cana-5313 165 9 , , PUNCT cana-5313 165 10 c. c. PROPN cana-5313 165 11 perez perez PROPN cana-5313 165 12 - - PUNCT cana-5313 165 13 galvan galvan PROPN cana-5313 165 14 , , PUNCT cana-5313 165 15 d. d. PROPN cana-5313 165 16 zhang zhang PROPN cana-5313 165 17 , , PUNCT cana-5313 165 18 a. a. PROPN cana-5313 165 19 del del PROPN cana-5313 165 20 rio rio PROPN cana-5313 165 21 chanona chanona PROPN cana-5313 165 22 , , PUNCT cana-5313 165 23 and and CCONJ cana-5313 165 24 f. f. PROPN cana-5313 165 25 j. j. PROPN cana-5313 165 26 navarro navarro PROPN cana-5313 165 27 - - PUNCT cana-5313 165 28 brull brull ADJ cana-5313 165 29 , , PUNCT cana-5313 165 30 β€œ " PUNCT cana-5313 165 31 industrial industrial ADJ cana-5313 165 32 data data NOUN cana-5313 165 33 science science NOUN cana-5313 165 34 – – PUNCT cana-5313 165 35 a a DET cana-5313 165 36 review review NOUN cana-5313 165 37 of of ADP cana-5313 165 38 machine machine NOUN cana-5313 165 39 learning learn VERB cana-5313 165 40 applications application NOUN cana-5313 165 41 for for ADP cana-5313 165 42 chemical chemical NOUN cana-5313 165 43 and and CCONJ cana-5313 165 44 process process NOUN cana-5313 165 45 industries industry NOUN cana-5313 165 46 , , PUNCT cana-5313 165 47 ” " PUNCT cana-5313 165 48 reaction reaction NOUN cana-5313 165 49 chemistry chemistry NOUN cana-5313 165 50 & & CCONJ cana-5313 165 51 engineering engineering PROPN cana-5313 165 52 , , PUNCT cana-5313 165 53 vol vol NOUN cana-5313 165 54 . . PROPN cana-5313 165 55 7 7 NUM cana-5313 165 56 , , PUNCT cana-5313 165 57 no no INTJ cana-5313 165 58 . . NOUN cana-5313 165 59 7 7 NUM cana-5313 165 60 , , PUNCT cana-5313 165 61 pp pp ADJ cana-5313 165 62 . . PUNCT cana-5313 166 1 1471–1509 1471–1509 NUM cana-5313 166 2 , , PUNCT cana-5313 166 3 jan jan PROPN cana-5313 166 4 . . PROPN cana-5313 166 5 2022 2022 NUM cana-5313 166 6 , , PUNCT cana-5313 166 7 doi doi NOUN cana-5313 166 8 : : PUNCT cana-5313 166 9 10.1039 10.1039 NUM cana-5313 166 10 / / SYM cana-5313 166 11 d1re00541c d1re00541c PROPN cana-5313 166 12 . . PUNCT cana-5313 167 1 [ [ X cana-5313 167 2 9 9 NUM cana-5313 167 3 ] ] X cana-5313 167 4 l. l. NOUN cana-5313 167 5 paninski paninski PROPN cana-5313 167 6 and and CCONJ cana-5313 167 7 j. j. PROPN cana-5313 167 8 cunningham cunningham PROPN cana-5313 167 9 , , PUNCT cana-5313 167 10 β€œ " PUNCT cana-5313 167 11 neural neural ADJ cana-5313 167 12 data datum NOUN cana-5313 167 13 science science NOUN cana-5313 167 14 : : PUNCT cana-5313 167 15 accelerating accelerate VERB cana-5313 167 16 the the DET cana-5313 167 17 experimentanalysis experimentanalysis NOUN cana-5313 167 18 - - PUNCT cana-5313 167 19 theory theory NOUN cana-5313 167 20 cycle cycle NOUN cana-5313 167 21 in in ADP cana-5313 167 22 large large ADJ cana-5313 167 23 - - PUNCT cana-5313 167 24 scale scale NOUN cana-5313 167 25 neuroscience neuroscience NOUN cana-5313 167 26 , , PUNCT cana-5313 167 27 ” " PUNCT cana-5313 167 28 current current ADJ cana-5313 167 29 opinion opinion NOUN cana-5313 167 30 in in ADP cana-5313 167 31 neurobiology neurobiology NOUN cana-5313 167 32 , , PUNCT cana-5313 167 33 vol vol NOUN cana-5313 167 34 . . PROPN cana-5313 167 35 50 50 NUM cana-5313 167 36 , , PUNCT cana-5313 167 37 pp pp ADJ cana-5313 167 38 . . PUNCT cana-5313 168 1 232–241 232–241 NUM cana-5313 168 2 , , PUNCT cana-5313 168 3 may may PROPN cana-5313 168 4 2018 2018 NUM cana-5313 168 5 , , PUNCT cana-5313 168 6 doi doi NOUN cana-5313 168 7 : : PUNCT cana-5313 168 8 10.1016 10.1016 NUM cana-5313 168 9 / / SYM cana-5313 168 10 j.conb.2018.04.007 j.conb.2018.04.007 PROPN cana-5313 168 11 . . PUNCT cana-5313 169 1 [ [ X cana-5313 169 2 10 10 NUM cana-5313 169 3 ] ] PUNCT cana-5313 169 4 a. a. NOUN cana-5313 169 5 malekloo malekloo NOUN cana-5313 169 6 , , PUNCT cana-5313 169 7 e. e. PROPN cana-5313 169 8 ozer ozer PROPN cana-5313 169 9 , , PUNCT cana-5313 169 10 m. m. NOUN cana-5313 169 11 alhamaydeh alhamaydeh NOUN cana-5313 169 12 , , PUNCT cana-5313 169 13 and and CCONJ cana-5313 169 14 m. m. NOUN cana-5313 169 15 girolami girolami NOUN cana-5313 169 16 , , PUNCT cana-5313 169 17 β€œ " PUNCT cana-5313 169 18 machine machine NOUN cana-5313 169 19 learning learning NOUN cana-5313 169 20 and and CCONJ cana-5313 169 21 structural structural ADJ cana-5313 169 22 health health NOUN cana-5313 169 23 monitoring monitoring NOUN cana-5313 169 24 overview overview NOUN cana-5313 169 25 with with ADP cana-5313 169 26 emerging emerge VERB cana-5313 169 27 technology technology NOUN cana-5313 169 28 and and CCONJ cana-5313 169 29 high high ADJ cana-5313 169 30 - - PUNCT cana-5313 169 31 dimensional dimensional ADJ cana-5313 169 32 data datum NOUN cana-5313 169 33 source source NOUN cana-5313 169 34 highlights highlight NOUN cana-5313 169 35 , , PUNCT cana-5313 169 36 ” " PUNCT cana-5313 169 37 structural structural ADJ cana-5313 169 38 health health NOUN cana-5313 169 39 monitoring monitoring NOUN cana-5313 169 40 , , PUNCT cana-5313 169 41 vol vol NOUN cana-5313 169 42 . . PROPN cana-5313 169 43 21 21 NUM cana-5313 169 44 , , PUNCT cana-5313 169 45 no no INTJ cana-5313 169 46 . . NOUN cana-5313 169 47 4 4 NUM cana-5313 169 48 , , PUNCT cana-5313 169 49 pp pp PROPN cana-5313 169 50 . . PUNCT cana-5313 170 1 1906–1955 1906–1955 NUM cana-5313 170 2 , , PUNCT cana-5313 170 3 aug aug PROPN cana-5313 170 4 . . PROPN cana-5313 170 5 2021 2021 NUM cana-5313 170 6 , , PUNCT cana-5313 170 7 doi doi NOUN cana-5313 170 8 : : PUNCT cana-5313 170 9 10.1177/14759217211036880 10.1177/14759217211036880 NUM cana-5313 170 10 . . PUNCT cana-5313 171 1 [ [ X cana-5313 171 2 11 11 NUM cana-5313 171 3 ] ] PUNCT cana-5313 171 4 s. s. PROPN cana-5313 171 5 raschka raschka PROPN cana-5313 171 6 , , PUNCT cana-5313 171 7 j. j. PROPN cana-5313 171 8 patterson patterson PROPN cana-5313 171 9 , , PUNCT cana-5313 171 10 and and CCONJ cana-5313 171 11 c. c. PROPN cana-5313 171 12 nolet nolet PROPN cana-5313 171 13 , , PUNCT cana-5313 171 14 β€œ " PUNCT cana-5313 171 15 machine machine NOUN cana-5313 171 16 learning learning NOUN cana-5313 171 17 in in ADP cana-5313 171 18 python python NOUN cana-5313 171 19 : : PUNCT cana-5313 171 20 main main ADJ cana-5313 171 21 developments development NOUN cana-5313 171 22 and and CCONJ cana-5313 171 23 technology technology NOUN cana-5313 171 24 trends trend NOUN cana-5313 171 25 in in ADP cana-5313 171 26 data data NOUN cana-5313 171 27 science science NOUN cana-5313 171 28 , , PUNCT cana-5313 171 29 machine machine NOUN cana-5313 171 30 learning learning NOUN cana-5313 171 31 , , PUNCT cana-5313 171 32 and and CCONJ cana-5313 171 33 artificial artificial ADJ cana-5313 171 34 intelligence intelligence NOUN cana-5313 171 35 , , PUNCT cana-5313 171 36 ” " PUNCT cana-5313 171 37 information information NOUN cana-5313 171 38 , , PUNCT cana-5313 171 39 vol vol NOUN cana-5313 171 40 . . PROPN cana-5313 171 41 11 11 NUM cana-5313 171 42 , , PUNCT cana-5313 171 43 no no INTJ cana-5313 171 44 . . NOUN cana-5313 171 45 4 4 NUM cana-5313 171 46 , , PUNCT cana-5313 171 47 p. p. NOUN cana-5313 171 48 193 193 NUM cana-5313 171 49 , , PUNCT cana-5313 171 50 apr apr PROPN cana-5313 171 51 . . PROPN cana-5313 171 52 2020 2020 NUM cana-5313 171 53 , , PUNCT cana-5313 171 54 doi doi NOUN cana-5313 171 55 : : PUNCT cana-5313 171 56 10.3390 10.3390 NUM cana-5313 171 57 / / SYM cana-5313 171 58 info11040193 info11040193 PROPN cana-5313 171 59 . . PUNCT cana-5313 172 1 [ [ X cana-5313 172 2 12 12 NUM cana-5313 172 3 ] ] PUNCT cana-5313 172 4 p. p. NOUN cana-5313 172 5 cui cui NOUN cana-5313 172 6 and and CCONJ cana-5313 172 7 s. s. PROPN cana-5313 172 8 athey athey PROPN cana-5313 172 9 , , PUNCT cana-5313 172 10 β€œ " PUNCT cana-5313 172 11 stable stable ADJ cana-5313 172 12 learning learning NOUN cana-5313 172 13 establishes establish VERB cana-5313 172 14 some some DET cana-5313 172 15 common common ADJ cana-5313 172 16 ground ground NOUN cana-5313 172 17 between between ADP cana-5313 172 18 causal causal NOUN cana-5313 172 19 inference inference NOUN cana-5313 172 20 and and CCONJ cana-5313 172 21 machine machine NOUN cana-5313 172 22 learning learning NOUN cana-5313 172 23 , , PUNCT cana-5313 172 24 ” " PUNCT cana-5313 172 25 nature nature NOUN cana-5313 172 26 machine machine NOUN cana-5313 172 27 intelligence intelligence NOUN cana-5313 172 28 , , PUNCT cana-5313 172 29 vol vol NOUN cana-5313 172 30 . . PROPN cana-5313 172 31 4 4 NUM cana-5313 172 32 , , PUNCT cana-5313 172 33 no no INTJ cana-5313 172 34 . . NOUN cana-5313 172 35 2 2 NUM cana-5313 172 36 , , PUNCT cana-5313 172 37 pp pp ADJ cana-5313 172 38 . . PUNCT cana-5313 173 1 110 110 NUM cana-5313 173 2 – – PUNCT cana-5313 173 3 115 115 NUM cana-5313 173 4 , , PUNCT cana-5313 173 5 feb feb PROPN cana-5313 173 6 . . PROPN cana-5313 173 7 2022 2022 NUM cana-5313 173 8 , , PUNCT cana-5313 173 9 doi doi NOUN cana-5313 173 10 : : PUNCT cana-5313 173 11 10.1038 10.1038 NUM cana-5313 173 12 / / SYM cana-5313 173 13 s42256 s42256 PROPN cana-5313 173 14 - - ADJ cana-5313 173 15 022 022 NUM cana-5313 173 16 - - PUNCT cana-5313 173 17 00445 00445 NUM cana-5313 174 1 - - PUNCT cana-5313 174 2 z. z. PROPN cana-5313 175 1 [ [ X cana-5313 175 2 13 13 NUM cana-5313 175 3 ] ] PUNCT cana-5313 175 4 a. a. NOUN cana-5313 175 5 goodman goodman PROPN cana-5313 175 6 , , PUNCT cana-5313 175 7 β€œ " PUNCT cana-5313 175 8 evolution evolution NOUN cana-5313 175 9 of of ADP cana-5313 175 10 symposia symposia NOUN cana-5313 175 11 on on ADP cana-5313 175 12 the the DET cana-5313 175 13 interface interface NOUN cana-5313 175 14 of of ADP cana-5313 175 15 computing computing NOUN cana-5313 175 16 and and CCONJ cana-5313 175 17 statistics statistic NOUN cana-5313 175 18 defines define VERB cana-5313 175 19 data datum NOUN cana-5313 175 20 science science NOUN cana-5313 175 21 to to PART cana-5313 175 22 be be AUX cana-5313 175 23 the the DET cana-5313 175 24 interface interface NOUN cana-5313 175 25 , , PUNCT cana-5313 175 26 ” " PUNCT cana-5313 175 27 wiley wiley PROPN cana-5313 175 28 interdisciplinary interdisciplinary ADJ cana-5313 175 29 reviews review NOUN cana-5313 175 30 computational computational ADJ cana-5313 175 31 statistics statistic NOUN cana-5313 175 32 , , PUNCT cana-5313 175 33 vol vol NOUN cana-5313 175 34 . . PROPN cana-5313 175 35 6 6 NUM cana-5313 175 36 , , PUNCT cana-5313 175 37 no no INTJ cana-5313 175 38 . . NOUN cana-5313 175 39 5 5 NUM cana-5313 175 40 , , PUNCT cana-5313 175 41 pp pp ADJ cana-5313 175 42 . . PUNCT cana-5313 176 1 367–377 367–377 NUM cana-5313 176 2 , , PUNCT cana-5313 176 3 jul jul PROPN cana-5313 176 4 . . PROPN cana-5313 176 5 2014 2014 NUM cana-5313 176 6 , , PUNCT cana-5313 176 7 doi doi NOUN cana-5313 176 8 : : PUNCT cana-5313 176 9 10.1002 10.1002 NUM cana-5313 176 10 / / SYM cana-5313 176 11 wics.1316 wics.1316 PROPN cana-5313 176 12 . . PUNCT cana-5313 177 1 [ [ X cana-5313 177 2 14 14 NUM cana-5313 177 3 ] ] X cana-5313 177 4 i. i. PROPN cana-5313 177 5 r. r. PROPN cana-5313 177 6 vogelius vogelius PROPN cana-5313 177 7 , , PUNCT cana-5313 177 8 j. j. PROPN cana-5313 177 9 petersen petersen PROPN cana-5313 177 10 , , PUNCT cana-5313 177 11 and and CCONJ cana-5313 177 12 s. s. PROPN cana-5313 177 13 m. m. PROPN cana-5313 177 14 bentzen bentzen PROPN cana-5313 177 15 , , PUNCT cana-5313 177 16 β€œ " PUNCT cana-5313 177 17 harnessing harness VERB cana-5313 177 18 data datum NOUN cana-5313 177 19 science science NOUN cana-5313 177 20 to to PART cana-5313 177 21 advance advance VERB cana-5313 177 22 radiation radiation NOUN cana-5313 177 23 oncology oncology NOUN cana-5313 177 24 , , PUNCT cana-5313 177 25 ” " PUNCT cana-5313 177 26 molecular molecular ADJ cana-5313 177 27 oncology oncology NOUN cana-5313 177 28 , , PUNCT cana-5313 177 29 vol vol NOUN cana-5313 177 30 . . PROPN cana-5313 177 31 14 14 NUM cana-5313 177 32 , , PUNCT cana-5313 177 33 no no INTJ cana-5313 177 34 . . NOUN cana-5313 177 35 7 7 NUM cana-5313 177 36 , , PUNCT cana-5313 177 37 pp pp ADJ cana-5313 177 38 . . PUNCT cana-5313 177 39 1514–1528 1514–1528 NUM cana-5313 177 40 , , PUNCT cana-5313 177 41 apr apr NOUN cana-5313 177 42 . . PROPN cana-5313 177 43 2020 2020 NUM cana-5313 177 44 , , PUNCT cana-5313 177 45 doi doi NOUN cana-5313 177 46 : : PUNCT cana-5313 177 47 10.1002/1878 10.1002/1878 NUM cana-5313 177 48 - - PUNCT cana-5313 177 49 0261.12685 0261.12685 NOUN cana-5313 177 50 . . PUNCT cana-5313 178 1 [ [ X cana-5313 178 2 15 15 NUM cana-5313 178 3 ] ] X cana-5313 178 4 j. j. PROPN cana-5313 178 5 desai desai PROPN cana-5313 178 6 , , PUNCT cana-5313 178 7 d. d. PROPN cana-5313 178 8 watson watson PROPN cana-5313 178 9 , , PUNCT cana-5313 178 10 v. v. PROPN cana-5313 178 11 wang wang PROPN cana-5313 178 12 , , PUNCT cana-5313 178 13 m. m. NOUN cana-5313 178 14 taddeo taddeo PROPN cana-5313 178 15 , , PUNCT cana-5313 178 16 and and CCONJ cana-5313 178 17 l. l. PROPN cana-5313 178 18 floridi floridi PROPN cana-5313 178 19 , , PUNCT cana-5313 178 20 β€œ " PUNCT cana-5313 178 21 the the DET cana-5313 178 22 epistemological epistemological ADJ cana-5313 178 23 foundations foundation NOUN cana-5313 178 24 of of ADP cana-5313 178 25 data datum NOUN cana-5313 178 26 science science NOUN cana-5313 178 27 : : PUNCT cana-5313 178 28 a a DET cana-5313 178 29 critical critical ADJ cana-5313 178 30 review review NOUN cana-5313 178 31 , , PUNCT cana-5313 178 32 ” " PUNCT cana-5313 178 33 synthese synthese ADJ cana-5313 178 34 , , PUNCT cana-5313 178 35 vol vol NOUN cana-5313 178 36 . . PROPN cana-5313 178 37 200 200 NUM cana-5313 178 38 , , PUNCT cana-5313 178 39 no no INTJ cana-5313 178 40 . . NOUN cana-5313 178 41 6 6 NUM cana-5313 178 42 , , PUNCT cana-5313 178 43 nov nov PROPN cana-5313 178 44 . . PROPN cana-5313 178 45 2022 2022 NUM cana-5313 178 46 , , PUNCT cana-5313 178 47 doi doi NOUN cana-5313 178 48 : : PUNCT cana-5313 178 49 10.1007 10.1007 NUM cana-5313 178 50 / / SYM cana-5313 178 51 s11229 s11229 NOUN cana-5313 178 52 - - PUNCT cana-5313 178 53 022 022 NUM cana-5313 178 54 - - PUNCT cana-5313 178 55 03933 03933 NUM cana-5313 178 56 - - PUNCT cana-5313 178 57 2 2 NUM cana-5313 178 58 . . PUNCT