id	sid	tid	token	lemma	pos
iajs-3245	1	1	392	392	NUM
iajs-3245	1	2	©	©	PROPN
iajs-3245	1	3	2024	2024	NUM
iajs-3245	1	4	the	the	DET
iajs-3245	1	5	author(s	author(s	NOUN
iajs-3245	1	6	)	)	PUNCT
iajs-3245	1	7	.	.	PUNCT
iajs-3245	2	1	published	publish	VERB
iajs-3245	2	2	by	by	ADP
iajs-3245	2	3	college	college	NOUN
iajs-3245	2	4	of	of	ADP
iajs-3245	2	5	education	education	NOUN
iajs-3245	2	6	for	for	ADP
iajs-3245	2	7	pure	pure	ADJ
iajs-3245	2	8	science	science	NOUN
iajs-3245	2	9	(	(	PUNCT
iajs-3245	2	10	ibn	ibn	PROPN
iajs-3245	2	11	al	al	PROPN
iajs-3245	2	12	-	-	PUNCT
iajs-3245	2	13	haitham	haitham	PROPN
iajs-3245	2	14	)	)	PUNCT
iajs-3245	2	15	,	,	PUNCT
iajs-3245	2	16	university	university	NOUN
iajs-3245	2	17	of	of	ADP
iajs-3245	2	18	baghdad	baghdad	PROPN
iajs-3245	2	19	.	.	PUNCT
iajs-3245	3	1	this	this	PRON
iajs-3245	3	2	is	be	AUX
iajs-3245	3	3	an	an	DET
iajs-3245	3	4	open	open	ADJ
iajs-3245	3	5	-	-	PUNCT
iajs-3245	3	6	access	access	NOUN
iajs-3245	3	7	article	article	NOUN
iajs-3245	3	8	distributed	distribute	VERB
iajs-3245	3	9	under	under	ADP
iajs-3245	3	10	the	the	DET
iajs-3245	3	11	terms	term	NOUN
iajs-3245	3	12	of	of	ADP
iajs-3245	3	13	the	the	DET
iajs-3245	3	14	creative	creative	ADJ
iajs-3245	3	15	commons	common	NOUN
iajs-3245	3	16	attribution	attribution	NOUN
iajs-3245	3	17	4.0	4.0	NUM
iajs-3245	3	18	international	international	ADJ
iajs-3245	3	19	license	license	NOUN
iajs-3245	3	20	ibn	ibn	PROPN
iajs-3245	3	21	al	al	PROPN
iajs-3245	3	22	-	-	PUNCT
iajs-3245	3	23	haitham	haitham	PROPN
iajs-3245	3	24	journal	journal	PROPN
iajs-3245	3	25	for	for	ADP
iajs-3245	3	26	pure	pure	ADJ
iajs-3245	3	27	and	and	CCONJ
iajs-3245	3	28	applied	applied	ADJ
iajs-3245	3	29	sciences	sciences	PROPN
iajs-3245	3	30	journal	journal	PROPN
iajs-3245	3	31	homepage	homepage	NOUN
iajs-3245	3	32	:	:	PUNCT
iajs-3245	3	33	jih.uobaghdad.edu.iq	jih.uobaghdad.edu.iq	NOUN
iajs-3245	3	34	pissn	pissn	ADJ
iajs-3245	3	35	:	:	PUNCT
iajs-3245	3	36	1609	1609	NUM
iajs-3245	3	37	-	-	SYM
iajs-3245	3	38	4042	4042	NUM
iajs-3245	3	39	,	,	PUNCT
iajs-3245	3	40	eissn	eissn	NOUN
iajs-3245	3	41	:	:	PUNCT
iajs-3245	3	42	2521	2521	NUM
iajs-3245	3	43	-	-	SYM
iajs-3245	3	44	3407	3407	NUM
iajs-3245	3	45	ihjpas	ihjpa	NOUN
iajs-3245	3	46	.	.	PUNCT
iajs-3245	4	1	2024	2024	NUM
iajs-3245	4	2	,	,	PUNCT
iajs-3245	4	3	37(4	37(4	X
iajs-3245	4	4	)	)	PUNCT
iajs-3245	4	5	a	a	DET
iajs-3245	4	6	modified	modify	VERB
iajs-3245	4	7	multivariate	multivariate	NOUN
iajs-3245	4	8	bayesian	bayesian	NOUN
iajs-3245	4	9	logistic	logistic	ADJ
iajs-3245	4	10	model	model	NOUN
iajs-3245	4	11	with	with	ADP
iajs-3245	4	12	application	application	NOUN
iajs-3245	4	13	to	to	ADP
iajs-3245	4	14	health	health	NOUN
iajs-3245	4	15	datasets	dataset	NOUN
iajs-3245	4	16	azza	azza	PROPN
iajs-3245	4	17	mustafa	mustafa	PROPN
iajs-3245	4	18	abd	abd	PROPN
iajs-3245	4	19	al	al	PROPN
iajs-3245	4	20	kader	kader	PROPN
iajs-3245	4	21	al	al	PROPN
iajs-3245	4	22	kusaem	kusaem	PROPN
iajs-3245	4	23	*	*	PROPN
iajs-3245	4	24	department	department	PROPN
iajs-3245	4	25	of	of	ADP
iajs-3245	4	26	business	business	PROPN
iajs-3245	4	27	administration	administration	PROPN
iajs-3245	4	28	,	,	PUNCT
iajs-3245	4	29	college	college	NOUN
iajs-3245	4	30	of	of	ADP
iajs-3245	4	31	administration	administration	NOUN
iajs-3245	4	32	and	and	CCONJ
iajs-3245	4	33	economics	economic	NOUN
iajs-3245	4	34	,	,	PUNCT
iajs-3245	4	35	university	university	NOUN
iajs-3245	4	36	of	of	ADP
iajs-3245	4	37	mosul	mosul	PROPN
iajs-3245	4	38	,	,	PUNCT
iajs-3245	4	39	mosul	mosul	PROPN
iajs-3245	4	40	,	,	PUNCT
iajs-3245	4	41	iraq	iraq	PROPN
iajs-3245	4	42	.	.	PUNCT
iajs-3245	5	1	*	*	PUNCT
iajs-3245	5	2	corresponding	correspond	VERB
iajs-3245	5	3	author	author	NOUN
iajs-3245	5	4	.	.	PUNCT
iajs-3245	6	1	received	receive	VERB
iajs-3245	6	2	:	:	PUNCT
iajs-3245	6	3	26	26	NUM
iajs-3245	6	4	january	january	PROPN
iajs-3245	6	5	2023	2023	NUM
iajs-3245	6	6	accepted	accept	VERB
iajs-3245	6	7	:	:	PUNCT
iajs-3245	6	8	16	16	NUM
iajs-3245	6	9	march	march	NOUN
iajs-3245	6	10	2023	2023	NUM
iajs-3245	6	11	published	publish	VERB
iajs-3245	6	12	:	:	PUNCT
iajs-3245	6	13	20	20	NUM
iajs-3245	6	14	october	october	NOUN
iajs-3245	6	15	2024	2024	NUM
iajs-3245	6	16	doi.org/10.30526/37.4.3245	doi.org/10.30526/37.4.3245	NOUN
iajs-3245	6	17	abstract	abstract	ADP
iajs-3245	6	18	the	the	DET
iajs-3245	6	19	application	application	NOUN
iajs-3245	6	20	of	of	ADP
iajs-3245	6	21	bayesian	bayesian	NOUN
iajs-3245	6	22	strategies	strategy	NOUN
iajs-3245	6	23	for	for	ADP
iajs-3245	6	24	binary	binary	ADJ
iajs-3245	6	25	logistic	logistic	ADJ
iajs-3245	6	26	estimation	estimation	NOUN
iajs-3245	6	27	is	be	AUX
iajs-3245	6	28	demonstrated	demonstrate	VERB
iajs-3245	6	29	in	in	ADP
iajs-3245	6	30	this	this	DET
iajs-3245	6	31	article	article	NOUN
iajs-3245	6	32	.	.	PUNCT
iajs-3245	7	1	a	a	DET
iajs-3245	7	2	modified	modify	VERB
iajs-3245	7	3	method	method	NOUN
iajs-3245	7	4	of	of	ADP
iajs-3245	7	5	the	the	DET
iajs-3245	7	6	bayesian	bayesian	NOUN
iajs-3245	7	7	logistic	logistic	ADJ
iajs-3245	7	8	model	model	NOUN
iajs-3245	7	9	using	use	VERB
iajs-3245	7	10	the	the	DET
iajs-3245	7	11	metropolis	metropolis	PROPN
iajs-3245	7	12	-	-	PUNCT
iajs-3245	7	13	hasting	hasting	PROPN
iajs-3245	7	14	algorithm	algorithm	PROPN
iajs-3245	7	15	is	be	AUX
iajs-3245	7	16	derived	derive	VERB
iajs-3245	7	17	and	and	CCONJ
iajs-3245	7	18	applied	apply	VERB
iajs-3245	7	19	to	to	ADP
iajs-3245	7	20	three	three	NUM
iajs-3245	7	21	simulation	simulation	NOUN
iajs-3245	7	22	data	data	NOUN
iajs-3245	7	23	sets	set	NOUN
iajs-3245	7	24	.	.	PUNCT
iajs-3245	8	1	we	we	PRON
iajs-3245	8	2	compared	compare	VERB
iajs-3245	8	3	the	the	DET
iajs-3245	8	4	new	new	ADJ
iajs-3245	8	5	model	model	NOUN
iajs-3245	8	6	with	with	ADP
iajs-3245	8	7	existing	exist	VERB
iajs-3245	8	8	classification	classification	NOUN
iajs-3245	8	9	methods	method	NOUN
iajs-3245	8	10	:	:	PUNCT
iajs-3245	8	11	support	support	NOUN
iajs-3245	8	12	vector	vector	NOUN
iajs-3245	8	13	machine	machine	NOUN
iajs-3245	8	14	,	,	PUNCT
iajs-3245	8	15	artificial	artificial	ADJ
iajs-3245	8	16	neural	neural	ADJ
iajs-3245	8	17	network	network	NOUN
iajs-3245	8	18	and	and	CCONJ
iajs-3245	8	19	regular	regular	ADJ
iajs-3245	8	20	logistic	logistic	ADJ
iajs-3245	8	21	model	model	NOUN
iajs-3245	8	22	.	.	PUNCT
iajs-3245	9	1	the	the	DET
iajs-3245	9	2	modified	modify	VERB
iajs-3245	9	3	model	model	NOUN
iajs-3245	9	4	was	be	AUX
iajs-3245	9	5	used	use	VERB
iajs-3245	9	6	to	to	PART
iajs-3245	9	7	classify	classify	VERB
iajs-3245	9	8	the	the	DET
iajs-3245	9	9	heart	heart	NOUN
iajs-3245	9	10	disease	disease	NOUN
iajs-3245	9	11	dataset	dataset	VERB
iajs-3245	9	12	.	.	PUNCT
iajs-3245	10	1	the	the	DET
iajs-3245	10	2	data	datum	NOUN
iajs-3245	10	3	came	come	VERB
iajs-3245	10	4	from	from	ADP
iajs-3245	10	5	a	a	DET
iajs-3245	10	6	database	database	NOUN
iajs-3245	10	7	intended	intend	VERB
iajs-3245	10	8	for	for	ADP
iajs-3245	10	9	uci	uci	PROPN
iajs-3245	10	10	data	data	PROPN
iajs-3245	10	11	science	science	NOUN
iajs-3245	10	12	(	(	PUNCT
iajs-3245	10	13	https://www.kaggle.com	https://www.kaggle.com	X
iajs-3245	10	14	)	)	PUNCT
iajs-3245	10	15	.	.	PUNCT
iajs-3245	11	1	the	the	DET
iajs-3245	11	2	clarification	clarification	NOUN
iajs-3245	11	3	accuracy	accuracy	NOUN
iajs-3245	11	4	and	and	CCONJ
iajs-3245	11	5	the	the	DET
iajs-3245	11	6	time	time	NOUN
iajs-3245	11	7	required	require	VERB
iajs-3245	11	8	are	be	AUX
iajs-3245	11	9	checked	check	VERB
iajs-3245	11	10	and	and	CCONJ
iajs-3245	11	11	compared	compare	VERB
iajs-3245	11	12	with	with	ADP
iajs-3245	11	13	other	other	ADJ
iajs-3245	11	14	standard	standard	ADJ
iajs-3245	11	15	methods	method	NOUN
iajs-3245	11	16	.	.	PUNCT
iajs-3245	12	1	it	it	PRON
iajs-3245	12	2	has	have	AUX
iajs-3245	12	3	been	be	AUX
iajs-3245	12	4	shown	show	VERB
iajs-3245	12	5	that	that	SCONJ
iajs-3245	12	6	the	the	DET
iajs-3245	12	7	presented	present	VERB
iajs-3245	12	8	model	model	NOUN
iajs-3245	12	9	has	have	VERB
iajs-3245	12	10	the	the	DET
iajs-3245	12	11	best	good	ADJ
iajs-3245	12	12	accuracy	accuracy	NOUN
iajs-3245	12	13	and	and	CCONJ
iajs-3245	12	14	efficiency	efficiency	NOUN
iajs-3245	12	15	compared	compare	VERB
iajs-3245	12	16	to	to	ADP
iajs-3245	12	17	the	the	DET
iajs-3245	12	18	different	different	ADJ
iajs-3245	12	19	classification	classification	NOUN
iajs-3245	12	20	methods	method	NOUN
iajs-3245	12	21	.	.	PUNCT
iajs-3245	13	1	all	all	DET
iajs-3245	13	2	calculations	calculation	NOUN
iajs-3245	13	3	were	be	AUX
iajs-3245	13	4	performed	perform	VERB
iajs-3245	13	5	with	with	ADP
iajs-3245	13	6	the	the	DET
iajs-3245	13	7	program	program	NOUN
iajs-3245	13	8	r	r	NOUN
iajs-3245	13	9	version	version	NOUN
iajs-3245	13	10	4.2.2	4.2.2	NOUN
iajs-3245	13	11	.	.	PUNCT
iajs-3245	14	1	keywords	keyword	NOUN
iajs-3245	14	2	:	:	PUNCT
iajs-3245	14	3	bayesian	bayesian	NOUN
iajs-3245	14	4	,	,	PUNCT
iajs-3245	14	5	classification	classification	NOUN
iajs-3245	14	6	,	,	PUNCT
iajs-3245	14	7	heart	heart	NOUN
iajs-3245	14	8	disease	disease	NOUN
iajs-3245	14	9	,	,	PUNCT
iajs-3245	14	10	logistic	logistic	ADJ
iajs-3245	14	11	regression	regression	NOUN
iajs-3245	14	12	,	,	PUNCT
iajs-3245	14	13	posterior	posterior	ADJ
iajs-3245	14	14	distribution	distribution	NOUN
iajs-3245	14	15	.	.	PUNCT
iajs-3245	15	1	1	1	X
iajs-3245	15	2	.	.	X
iajs-3245	15	3	introduction	introduction	NOUN
iajs-3245	15	4	the	the	DET
iajs-3245	15	5	bayesian	bayesian	NOUN
iajs-3245	15	6	algorithm	algorithm	NOUN
iajs-3245	15	7	,	,	PUNCT
iajs-3245	15	8	markov	markov	NOUN
iajs-3245	15	9	chain	chain	NOUN
iajs-3245	15	10	monte	monte	PROPN
iajs-3245	15	11	carlo	carlo	PROPN
iajs-3245	15	12	(	(	PUNCT
iajs-3245	15	13	mcmc	mcmc	PROPN
iajs-3245	15	14	)	)	PUNCT
iajs-3245	15	15	,	,	PUNCT
iajs-3245	15	16	is	be	AUX
iajs-3245	15	17	a	a	DET
iajs-3245	15	18	widely	widely	ADV
iajs-3245	15	19	used	use	VERB
iajs-3245	15	20	technical	technical	ADJ
iajs-3245	15	21	algorithm	algorithm	NOUN
iajs-3245	15	22	used	use	VERB
iajs-3245	15	23	to	to	PART
iajs-3245	15	24	estimate	estimate	VERB
iajs-3245	15	25	the	the	DET
iajs-3245	15	26	parameters	parameter	NOUN
iajs-3245	15	27	of	of	ADP
iajs-3245	15	28	the	the	DET
iajs-3245	15	29	posterior	posterior	ADJ
iajs-3245	15	30	distribution	distribution	NOUN
iajs-3245	15	31	in	in	ADP
iajs-3245	15	32	the	the	DET
iajs-3245	15	33	model	model	NOUN
iajs-3245	15	34	[	[	X
iajs-3245	15	35	1	1	NUM
iajs-3245	15	36	,	,	PUNCT
iajs-3245	15	37	2	2	NUM
iajs-3245	15	38	]	]	PUNCT
iajs-3245	15	39	.	.	PUNCT
iajs-3245	16	1	the	the	DET
iajs-3245	16	2	standard	standard	ADJ
iajs-3245	16	3	estimation	estimation	NOUN
iajs-3245	16	4	of	of	ADP
iajs-3245	16	5	the	the	DET
iajs-3245	16	6	logistic	logistic	ADJ
iajs-3245	16	7	regression	regression	NOUN
iajs-3245	16	8	model	model	NOUN
iajs-3245	16	9	has	have	VERB
iajs-3245	16	10	some	some	DET
iajs-3245	16	11	limitations	limitation	NOUN
iajs-3245	16	12	that	that	PRON
iajs-3245	16	13	can	can	AUX
iajs-3245	16	14	be	be	AUX
iajs-3245	16	15	overcome	overcome	VERB
iajs-3245	16	16	with	with	ADP
iajs-3245	16	17	possible	possible	ADJ
iajs-3245	16	18	replacement	replacement	NOUN
iajs-3245	16	19	methods	method	NOUN
iajs-3245	16	20	.	.	PUNCT
iajs-3245	17	1	this	this	DET
iajs-3245	17	2	paper	paper	NOUN
iajs-3245	17	3	aims	aim	VERB
iajs-3245	17	4	to	to	PART
iajs-3245	17	5	introduce	introduce	VERB
iajs-3245	17	6	a	a	DET
iajs-3245	17	7	different	different	ADJ
iajs-3245	17	8	approach	approach	NOUN
iajs-3245	17	9	using	use	VERB
iajs-3245	17	10	bayesian	bayesian	NOUN
iajs-3245	17	11	analysis	analysis	NOUN
iajs-3245	17	12	.	.	PUNCT
iajs-3245	18	1	multinomial	multinomial	ADJ
iajs-3245	18	2	logistic	logistic	ADJ
iajs-3245	18	3	regression	regression	NOUN
iajs-3245	18	4	can	can	AUX
iajs-3245	18	5	predict	predict	VERB
iajs-3245	18	6	categorical	categorical	ADJ
iajs-3245	18	7	placement	placement	NOUN
iajs-3245	18	8	based	base	VERB
iajs-3245	18	9	on	on	ADP
iajs-3245	18	10	multiple	multiple	ADJ
iajs-3245	18	11	independent	independent	ADJ
iajs-3245	18	12	variables	variable	NOUN
iajs-3245	18	13	,	,	PUNCT
iajs-3245	18	14	such	such	ADJ
iajs-3245	18	15	as	as	ADP
iajs-3245	18	16	the	the	DET
iajs-3245	18	17	possibility	possibility	NOUN
iajs-3245	18	18	of	of	ADP
iajs-3245	18	19	belonging	belong	VERB
iajs-3245	18	20	to	to	ADP
iajs-3245	18	21	a	a	DET
iajs-3245	18	22	category	category	NOUN
iajs-3245	18	23	on	on	ADP
iajs-3245	18	24	a	a	DET
iajs-3245	18	25	related	related	ADJ
iajs-3245	18	26	variable	variable	NOUN
iajs-3245	18	27	.	.	PUNCT
iajs-3245	19	1	the	the	DET
iajs-3245	19	2	independent	independent	ADJ
iajs-3245	19	3	variables	variable	NOUN
iajs-3245	19	4	can	can	AUX
iajs-3245	19	5	be	be	AUX
iajs-3245	19	6	either	either	CCONJ
iajs-3245	19	7	categorical	categorical	ADJ
iajs-3245	19	8	or	or	CCONJ
iajs-3245	19	9	continuous	continuous	ADJ
iajs-3245	19	10	.	.	PUNCT
iajs-3245	20	1	multinomial	multinomial	ADJ
iajs-3245	20	2	logistic	logistic	ADJ
iajs-3245	20	3	regression	regression	NOUN
iajs-3245	20	4	is	be	AUX
iajs-3245	20	5	often	often	ADV
iajs-3245	20	6	considered	consider	VERB
iajs-3245	20	7	an	an	DET
iajs-3245	20	8	appealing	appealing	ADJ
iajs-3245	20	9	analysis	analysis	NOUN
iajs-3245	20	10	because	because	SCONJ
iajs-3245	20	11	it	it	PRON
iajs-3245	20	12	does	do	AUX
iajs-3245	20	13	not	not	PART
iajs-3245	20	14	require	require	VERB
iajs-3245	20	15	homoscedasticity	homoscedasticity	NOUN
iajs-3245	20	16	,	,	PUNCT
iajs-3245	20	17	linearity	linearity	NOUN
iajs-3245	20	18	,	,	PUNCT
iajs-3245	20	19	or	or	CCONJ
iajs-3245	20	20	normality	normality	NOUN
iajs-3245	21	1	[	[	X
iajs-3245	21	2	3	3	NUM
iajs-3245	21	3	]	]	PUNCT
iajs-3245	21	4	.	.	PUNCT
iajs-3245	22	1	discriminant	discriminant	PROPN
iajs-3245	22	2	function	function	NOUN
iajs-3245	22	3	analysis	analysis	NOUN
iajs-3245	22	4	is	be	AUX
iajs-3245	22	5	an	an	DET
iajs-3245	22	6	excellent	excellent	ADJ
iajs-3245	22	7	substitute	substitute	NOUN
iajs-3245	22	8	for	for	ADP
iajs-3245	22	9	multinomial	multinomial	ADJ
iajs-3245	22	10	logistic	logistic	ADJ
iajs-3245	22	11	regression	regression	NOUN
iajs-3245	22	12	,	,	PUNCT
iajs-3245	22	13	which	which	PRON
iajs-3245	22	14	requires	require	VERB
iajs-3245	22	15	these	these	DET
iajs-3245	22	16	assumptions	assumption	NOUN
iajs-3245	22	17	.	.	PUNCT
iajs-3245	23	1	one	one	NUM
iajs-3245	23	2	of	of	ADP
iajs-3245	23	3	the	the	DET
iajs-3245	23	4	most	most	ADV
iajs-3245	23	5	common	common	ADJ
iajs-3245	23	6	classification	classification	NOUN
iajs-3245	23	7	methods	method	NOUN
iajs-3245	23	8	for	for	ADP
iajs-3245	23	9	health	health	NOUN
iajs-3245	23	10	datasets	dataset	NOUN
iajs-3245	23	11	is	be	AUX
iajs-3245	23	12	logistic	logistic	ADJ
iajs-3245	23	13	regression	regression	NOUN
iajs-3245	23	14	.	.	PUNCT
iajs-3245	24	1	the	the	DET
iajs-3245	24	2	maximum	maximum	ADJ
iajs-3245	24	3	likelihood	likelihood	NOUN
iajs-3245	24	4	or	or	CCONJ
iajs-3245	24	5	ordinary	ordinary	ADJ
iajs-3245	24	6	least	least	ADJ
iajs-3245	24	7	square	square	ADJ
iajs-3245	24	8	estimator	estimator	NOUN
iajs-3245	24	9	can	can	AUX
iajs-3245	24	10	be	be	AUX
iajs-3245	24	11	used	use	VERB
iajs-3245	24	12	to	to	PART
iajs-3245	24	13	estimate	estimate	VERB
iajs-3245	24	14	the	the	DET
iajs-3245	24	15	regression	regression	NOUN
iajs-3245	24	16	coefficients	coefficient	VERB
iajs-3245	24	17	[	[	X
iajs-3245	24	18	4	4	NUM
iajs-3245	24	19	,	,	PUNCT
iajs-3245	24	20	5	5	NUM
iajs-3245	24	21	]	]	PUNCT
iajs-3245	24	22	.	.	PUNCT
iajs-3245	25	1	https://creativecommons.org/licenses/by/4.0/	https://creativecommons.org/licenses/by/4.0/	PROPN
iajs-3245	25	2	https://creativecommons.org/licenses/by/4.0/	https://creativecommons.org/licenses/by/4.0/	PROPN
iajs-3245	25	3	https://orcid.org/0000-0003-2322-8991	https://orcid.org/0000-0003-2322-8991	PROPN
iajs-3245	25	4	mailto:aza.mustafa@uomosul.edu.iq	mailto:aza.mustafa@uomosul.edu.iq	PROPN
iajs-3245	25	5	ihjpas	ihjpas	PROPN
iajs-3245	25	6	.	.	PUNCT
iajs-3245	26	1	2024	2024	NUM
iajs-3245	26	2	,	,	PUNCT
iajs-3245	26	3	37(4	37(4	PRON
iajs-3245	26	4	)	)	PUNCT
iajs-3245	26	5	393	393	NUM
iajs-3245	26	6	the	the	DET
iajs-3245	26	7	dependent	dependent	ADJ
iajs-3245	26	8	variable	variable	NOUN
iajs-3245	26	9	in	in	ADP
iajs-3245	26	10	logistic	logistic	ADJ
iajs-3245	26	11	regression	regression	NOUN
iajs-3245	26	12	is	be	AUX
iajs-3245	26	13	either	either	CCONJ
iajs-3245	26	14	binary	binary	ADJ
iajs-3245	26	15	or	or	CCONJ
iajs-3245	26	16	dichotomous	dichotomous	ADJ
iajs-3245	26	17	,	,	PUNCT
iajs-3245	26	18	i.e.	i.e.	X
iajs-3245	26	19	the	the	DET
iajs-3245	26	20	logistic	logistic	ADJ
iajs-3245	26	21	regression	regression	NOUN
iajs-3245	26	22	contains	contain	VERB
iajs-3245	26	23	only	only	ADV
iajs-3245	26	24	data	datum	NOUN
iajs-3245	26	25	coded	code	VERB
iajs-3245	26	26	as	as	ADP
iajs-3245	26	27	“	"	PUNCT
iajs-3245	26	28	1	1	NUM
iajs-3245	26	29	”	"	PUNCT
iajs-3245	26	30	(	(	PUNCT
iajs-3245	26	31	true	true	ADJ
iajs-3245	26	32	,	,	PUNCT
iajs-3245	26	33	yes	yes	INTJ
iajs-3245	26	34	,	,	PUNCT
iajs-3245	26	35	healthy	healthy	ADJ
iajs-3245	26	36	,	,	PUNCT
iajs-3245	26	37	success	success	NOUN
iajs-3245	26	38	,	,	PUNCT
iajs-3245	26	39	pregnant	pregnant	ADJ
iajs-3245	26	40	,	,	PUNCT
iajs-3245	26	41	etc	etc	X
iajs-3245	26	42	.	.	X
iajs-3245	26	43	)	)	PUNCT
iajs-3245	26	44	or	or	CCONJ
iajs-3245	26	45	“	"	PUNCT
iajs-3245	26	46	0	0	NUM
iajs-3245	26	47	”	"	PUNCT
iajs-3245	26	48	(	(	PUNCT
iajs-3245	26	49	false	false	ADJ
iajs-3245	26	50	,	,	PUNCT
iajs-3245	26	51	no	no	INTJ
iajs-3245	26	52	,	,	PUNCT
iajs-3245	26	53	sick	sick	ADJ
iajs-3245	26	54	,	,	PUNCT
iajs-3245	26	55	failure	failure	NOUN
iajs-3245	26	56	,	,	PUNCT
iajs-3245	26	57	not	not	PART
iajs-3245	26	58	pregnant	pregnant	ADJ
iajs-3245	26	59	,	,	PUNCT
iajs-3245	26	60	etc	etc	X
iajs-3245	26	61	.	.	X
iajs-3245	26	62	)	)	PUNCT
iajs-3245	27	1	[	[	X
iajs-3245	27	2	6	6	NUM
iajs-3245	27	3	]	]	PUNCT
iajs-3245	27	4	.	.	PUNCT
iajs-3245	28	1	many	many	ADJ
iajs-3245	28	2	standard	standard	ADJ
iajs-3245	28	3	classifications	classification	NOUN
iajs-3245	28	4	such	such	ADJ
iajs-3245	28	5	as	as	ADP
iajs-3245	28	6	support	support	NOUN
iajs-3245	28	7	vector	vector	NOUN
iajs-3245	28	8	machine	machine	NOUN
iajs-3245	28	9	(	(	PUNCT
iajs-3245	28	10	svm	svm	PROPN
iajs-3245	28	11	)	)	PUNCT
iajs-3245	28	12	[	[	X
iajs-3245	28	13	2	2	NUM
iajs-3245	28	14	,	,	PUNCT
iajs-3245	28	15	7	7	NUM
iajs-3245	28	16	]	]	PUNCT
iajs-3245	28	17	,	,	PUNCT
iajs-3245	28	18	artificial	artificial	ADJ
iajs-3245	28	19	neural	neural	ADJ
iajs-3245	28	20	networks	network	NOUN
iajs-3245	28	21	(	(	PUNCT
iajs-3245	28	22	ann	ann	PROPN
iajs-3245	28	23	)	)	PUNCT
iajs-3245	29	1	[	[	X
iajs-3245	29	2	8	8	NUM
iajs-3245	29	3	]	]	PUNCT
iajs-3245	29	4	and	and	CCONJ
iajs-3245	29	5	regular	regular	ADJ
iajs-3245	29	6	logistic	logistic	ADJ
iajs-3245	29	7	regression	regression	NOUN
iajs-3245	29	8	[	[	X
iajs-3245	29	9	9	9	NUM
iajs-3245	29	10	]	]	PUNCT
iajs-3245	29	11	can	can	AUX
iajs-3245	29	12	be	be	AUX
iajs-3245	29	13	used	use	VERB
iajs-3245	29	14	for	for	ADP
iajs-3245	29	15	classification	classification	NOUN
iajs-3245	29	16	.	.	PUNCT
iajs-3245	30	1	however	however	ADV
iajs-3245	30	2	,	,	PUNCT
iajs-3245	30	3	working	work	VERB
iajs-3245	30	4	with	with	ADP
iajs-3245	30	5	a	a	DET
iajs-3245	30	6	large	large	ADJ
iajs-3245	30	7	dataset	dataset	NOUN
iajs-3245	30	8	leads	lead	VERB
iajs-3245	30	9	to	to	ADP
iajs-3245	30	10	inefficiency	inefficiency	NOUN
iajs-3245	30	11	and	and	CCONJ
iajs-3245	30	12	time	time	NOUN
iajs-3245	30	13	consumption	consumption	NOUN
iajs-3245	30	14	[	[	X
iajs-3245	30	15	10	10	NUM
iajs-3245	30	16	,	,	PUNCT
iajs-3245	30	17	11	11	NUM
iajs-3245	30	18	]	]	PUNCT
iajs-3245	30	19	.	.	PUNCT
iajs-3245	31	1	this	this	DET
iajs-3245	31	2	paper	paper	NOUN
iajs-3245	31	3	presents	present	VERB
iajs-3245	31	4	a	a	DET
iajs-3245	31	5	new	new	ADJ
iajs-3245	31	6	bayesian	bayesian	NOUN
iajs-3245	31	7	multivariate	multivariate	NOUN
iajs-3245	31	8	model	model	NOUN
iajs-3245	31	9	for	for	ADP
iajs-3245	31	10	classifying	classify	VERB
iajs-3245	31	11	datasets	dataset	NOUN
iajs-3245	31	12	.	.	PUNCT
iajs-3245	32	1	the	the	DET
iajs-3245	32	2	modified	modify	VERB
iajs-3245	32	3	model	model	NOUN
iajs-3245	32	4	is	be	AUX
iajs-3245	32	5	applied	apply	VERB
iajs-3245	32	6	to	to	ADP
iajs-3245	32	7	three	three	NUM
iajs-3245	32	8	simulation	simulation	NOUN
iajs-3245	32	9	datasets	dataset	NOUN
iajs-3245	32	10	and	and	CCONJ
iajs-3245	32	11	then	then	ADV
iajs-3245	32	12	used	use	VERB
iajs-3245	32	13	to	to	PART
iajs-3245	32	14	classify	classify	VERB
iajs-3245	32	15	the	the	DET
iajs-3245	32	16	heart	heart	NOUN
iajs-3245	32	17	disease	disease	NOUN
iajs-3245	32	18	dataset	dataset	VERB
iajs-3245	32	19	.	.	PUNCT
iajs-3245	33	1	this	this	DET
iajs-3245	33	2	article	article	NOUN
iajs-3245	33	3	is	be	AUX
iajs-3245	33	4	structured	structure	VERB
iajs-3245	33	5	as	as	SCONJ
iajs-3245	33	6	follows	follow	VERB
iajs-3245	33	7	:	:	PUNCT
iajs-3245	33	8	in	in	ADP
iajs-3245	33	9	section	section	NOUN
iajs-3245	33	10	two	two	NUM
iajs-3245	33	11	,	,	PUNCT
iajs-3245	33	12	a	a	DET
iajs-3245	33	13	general	general	ADJ
iajs-3245	33	14	idea	idea	NOUN
iajs-3245	33	15	for	for	SCONJ
iajs-3245	33	16	multivariate	multivariate	NOUN
iajs-3245	33	17	bayesian	bayesian	NOUN
iajs-3245	33	18	binary	binary	NOUN
iajs-3245	33	19	logistic	logistic	ADJ
iajs-3245	33	20	linear	linear	PROPN
iajs-3245	33	21	regression	regression	NOUN
iajs-3245	33	22	is	be	AUX
iajs-3245	33	23	explained	explain	VERB
iajs-3245	33	24	.	.	PUNCT
iajs-3245	34	1	the	the	DET
iajs-3245	34	2	bayesian	bayesian	NOUN
iajs-3245	34	3	formulation	formulation	NOUN
iajs-3245	34	4	model	model	NOUN
iajs-3245	34	5	is	be	AUX
iajs-3245	34	6	discussed	discuss	VERB
iajs-3245	34	7	in	in	ADP
iajs-3245	34	8	section	section	NOUN
iajs-3245	34	9	three	three	NUM
iajs-3245	34	10	.	.	PUNCT
iajs-3245	35	1	simulation	simulation	NOUN
iajs-3245	35	2	studies	study	NOUN
iajs-3245	35	3	and	and	CCONJ
iajs-3245	35	4	experimental	experimental	ADJ
iajs-3245	35	5	results	result	NOUN
iajs-3245	35	6	are	be	AUX
iajs-3245	35	7	presented	present	VERB
iajs-3245	35	8	in	in	ADP
iajs-3245	35	9	section	section	NOUN
iajs-3245	35	10	four	four	NUM
iajs-3245	35	11	.	.	PUNCT
iajs-3245	36	1	the	the	DET
iajs-3245	36	2	real	real	ADJ
iajs-3245	36	3	data	datum	NOUN
iajs-3245	36	4	set	set	VERB
iajs-3245	36	5	is	be	AUX
iajs-3245	36	6	presented	present	VERB
iajs-3245	36	7	in	in	ADP
iajs-3245	36	8	section	section	NOUN
iajs-3245	36	9	five	five	NUM
iajs-3245	36	10	.	.	PUNCT
iajs-3245	37	1	section	section	NOUN
iajs-3245	37	2	six	six	NUM
iajs-3245	37	3	discusses	discuss	VERB
iajs-3245	37	4	the	the	DET
iajs-3245	37	5	results	result	NOUN
iajs-3245	37	6	.	.	PUNCT
iajs-3245	38	1	2	2	X
iajs-3245	38	2	.	.	X
iajs-3245	38	3	multivariate	multivariate	NOUN
iajs-3245	38	4	bayesian	bayesian	NOUN
iajs-3245	38	5	binary	binary	PROPN
iajs-3245	38	6	logistic	logistic	PROPN
iajs-3245	38	7	regression	regression	NOUN
iajs-3245	38	8	model	model	NOUN
iajs-3245	38	9	binary	binary	PROPN
iajs-3245	38	10	logistic	logistic	PROPN
iajs-3245	38	11	regression	regression	NOUN
iajs-3245	38	12	is	be	AUX
iajs-3245	38	13	a	a	DET
iajs-3245	38	14	special	special	ADJ
iajs-3245	38	15	form	form	NOUN
iajs-3245	38	16	of	of	ADP
iajs-3245	38	17	regression	regression	NOUN
iajs-3245	38	18	in	in	ADP
iajs-3245	38	19	which	which	PRON
iajs-3245	38	20	the	the	DET
iajs-3245	38	21	binary	binary	ADJ
iajs-3245	38	22	response	response	NOUN
iajs-3245	38	23	variable	variable	NOUN
iajs-3245	38	24	is	be	AUX
iajs-3245	38	25	linked	link	VERB
iajs-3245	38	26	to	to	ADP
iajs-3245	38	27	a	a	DET
iajs-3245	38	28	discrete	discrete	ADJ
iajs-3245	38	29	or	or	CCONJ
iajs-3245	38	30	continuous	continuous	ADJ
iajs-3245	38	31	set	set	NOUN
iajs-3245	38	32	of	of	ADP
iajs-3245	38	33	explanatory	explanatory	ADJ
iajs-3245	38	34	variables	variable	NOUN
iajs-3245	38	35	.	.	PUNCT
iajs-3245	39	1	the	the	DET
iajs-3245	39	2	key	key	ADJ
iajs-3245	39	3	point	point	NOUN
iajs-3245	39	4	here	here	ADV
iajs-3245	39	5	is	be	AUX
iajs-3245	39	6	that	that	SCONJ
iajs-3245	39	7	the	the	DET
iajs-3245	39	8	predicted	predict	VERB
iajs-3245	39	9	values	value	NOUN
iajs-3245	39	10	of	of	ADP
iajs-3245	39	11	the	the	DET
iajs-3245	39	12	response	response	NOUN
iajs-3245	39	13	variable	variable	NOUN
iajs-3245	39	14	are	be	AUX
iajs-3245	39	15	modelled	model	VERB
iajs-3245	39	16	based	base	VERB
iajs-3245	39	17	on	on	ADP
iajs-3245	39	18	the	the	DET
iajs-3245	39	19	mixture	mixture	NOUN
iajs-3245	39	20	of	of	ADP
iajs-3245	39	21	values	value	NOUN
iajs-3245	39	22	provided	provide	VERB
iajs-3245	39	23	by	by	ADP
iajs-3245	39	24	the	the	DET
iajs-3245	39	25	predictors	predictor	NOUN
iajs-3245	39	26	in	in	ADP
iajs-3245	39	27	linear	linear	ADJ
iajs-3245	39	28	regression	regression	NOUN
iajs-3245	39	29	[	[	X
iajs-3245	39	30	12	12	NUM
iajs-3245	39	31	]	]	PUNCT
iajs-3245	39	32	.	.	PUNCT
iajs-3245	40	1	let	let	VERB
iajs-3245	40	2	𝑋	𝑋	PROPN
iajs-3245	40	3	=	=	SYM
iajs-3245	40	4	(	(	PUNCT
iajs-3245	40	5	𝑋1	𝑋1	PROPN
iajs-3245	40	6	,	,	PUNCT
iajs-3245	40	7	𝑋2	𝑋2	VERB
iajs-3245	40	8	,	,	PUNCT
iajs-3245	40	9	…	…	PUNCT
iajs-3245	40	10	,	,	PUNCT
iajs-3245	40	11	𝑋𝑝	𝑋𝑝	NOUN
iajs-3245	40	12	)	)	PUNCT
iajs-3245	40	13	be	be	AUX
iajs-3245	40	14	a	a	DET
iajs-3245	40	15	set	set	NOUN
iajs-3245	40	16	of	of	ADP
iajs-3245	40	17	𝑝	𝑝	NOUN
iajs-3245	40	18	explanatory	explanatory	ADJ
iajs-3245	40	19	variables	variable	NOUN
iajs-3245	40	20	and	and	CCONJ
iajs-3245	40	21	𝑌	𝑌	PROPN
iajs-3245	40	22	be	be	VERB
iajs-3245	40	23	a	a	DET
iajs-3245	40	24	binary	binary	ADJ
iajs-3245	40	25	response	response	NOUN
iajs-3245	40	26	variable	variable	NOUN
iajs-3245	40	27	;	;	PUNCT
iajs-3245	40	28	then	then	ADV
iajs-3245	40	29	the	the	DET
iajs-3245	40	30	binary	binary	PROPN
iajs-3245	40	31	logistic	logistic	PROPN
iajs-3245	40	32	regression	regression	NOUN
iajs-3245	40	33	model	model	NOUN
iajs-3245	40	34	can	can	AUX
iajs-3245	40	35	be	be	AUX
iajs-3245	40	36	written	write	VERB
iajs-3245	40	37	as	as	SCONJ
iajs-3245	40	38	follows	follow	VERB
iajs-3245	40	39	,	,	PUNCT
iajs-3245	40	40	𝑙𝑜𝑔𝑖𝑡(𝑝	𝑙𝑜𝑔𝑖𝑡(𝑝	ADJ
iajs-3245	40	41	)	)	PUNCT
iajs-3245	41	1	=	=	SYM
iajs-3245	41	2	log	log	NOUN
iajs-3245	41	3	(	(	PUNCT
iajs-3245	41	4	𝑝	𝑝	NOUN
iajs-3245	41	5	1−𝑝	1−𝑝	NUM
iajs-3245	41	6	)	)	PUNCT
iajs-3245	42	1	=	=	SYM
iajs-3245	42	2	𝛽0	𝛽0	PROPN
iajs-3245	42	3	+	+	CCONJ
iajs-3245	42	4	𝛽𝑥𝑖	𝛽𝑥𝑖	ADJ
iajs-3245	42	5	+	+	NUM
iajs-3245	42	6	⋯	⋯	PROPN
iajs-3245	42	7	+	+	CCONJ
iajs-3245	42	8	𝛽0	𝛽0	NOUN
iajs-3245	42	9	+	+	CCONJ
iajs-3245	42	10	𝛽𝑋𝑘′	𝛽𝑋𝑘′	ADP
iajs-3245	42	11	(	(	PUNCT
iajs-3245	42	12	1	1	X
iajs-3245	42	13	)	)	PUNCT
iajs-3245	42	14	which	which	PRON
iajs-3245	42	15	models	model	VERB
iajs-3245	42	16	the	the	DET
iajs-3245	42	17	log	log	NOUN
iajs-3245	42	18	odds	odd	NOUN
iajs-3245	42	19	of	of	ADP
iajs-3245	42	20	the	the	DET
iajs-3245	42	21	probability	probability	NOUN
iajs-3245	42	22	of	of	ADP
iajs-3245	42	23	“	"	PUNCT
iajs-3245	42	24	present	present	ADJ
iajs-3245	42	25	”	"	PUNCT
iajs-3245	42	26	as	as	ADP
iajs-3245	42	27	a	a	DET
iajs-3245	42	28	function	function	NOUN
iajs-3245	42	29	of	of	ADP
iajs-3245	42	30	explanatory	explanatory	ADJ
iajs-3245	42	31	variables	variable	NOUN
iajs-3245	42	32	.	.	PUNCT
iajs-3245	43	1	assume	assume	VERB
iajs-3245	43	2	that	that	SCONJ
iajs-3245	43	3	𝑌𝑖	𝑌𝑖	PROPN
iajs-3245	43	4	∼	∼	NOUN
iajs-3245	43	5	𝐵𝑖𝑛(𝑛𝑖	𝐵𝑖𝑛(𝑛𝑖	NOUN
iajs-3245	43	6	,	,	PUNCT
iajs-3245	43	7	𝑝𝑖	𝑝𝑖	NOUN
iajs-3245	43	8	)	)	PUNCT
iajs-3245	43	9	,	,	PUNCT
iajs-3245	43	10	i.e.	i.e.	X
iajs-3245	43	11	,	,	PUNCT
iajs-3245	43	12	𝑌𝑖	𝑌𝑖	PROPN
iajs-3245	43	13	is	be	AUX
iajs-3245	43	14	a	a	DET
iajs-3245	43	15	binary	binary	ADJ
iajs-3245	43	16	logistic	logistic	ADJ
iajs-3245	43	17	model	model	NOUN
iajs-3245	43	18	,	,	PUNCT
iajs-3245	43	19	then	then	ADV
iajs-3245	43	20	𝜋𝑖	𝜋𝑖	ADP
iajs-3245	43	21	=	=	PUNCT
iajs-3245	43	22	𝑃𝑟𝑜(𝑌𝑖	𝑃𝑟𝑜(𝑌𝑖	PROPN
iajs-3245	43	23	=	=	SYM
iajs-3245	43	24	1|𝑋𝑖	1|𝑋𝑖	NUM
iajs-3245	43	25	=	=	SYM
iajs-3245	43	26	𝑥𝑖	𝑥𝑖	PROPN
iajs-3245	43	27	)	)	PUNCT
iajs-3245	43	28	=	=	VERB
iajs-3245	44	1	𝑒𝛽0+𝛽1𝑥𝑖	𝑒𝛽0+𝛽1𝑥𝑖	ADJ
iajs-3245	44	2	1	1	NUM
iajs-3245	44	3	+	+	NUM
iajs-3245	44	4	𝑒𝛽0+𝛽1𝑥𝑖	𝑒𝛽0+𝛽1𝑥𝑖	ADJ
iajs-3245	44	5	(	(	PUNCT
iajs-3245	44	6	2	2	NUM
iajs-3245	44	7	)	)	PUNCT
iajs-3245	44	8	the	the	DET
iajs-3245	44	9	maximum	maximum	ADJ
iajs-3245	44	10	likelihood	likelihood	NOUN
iajs-3245	44	11	estimator	estimator	NOUN
iajs-3245	44	12	(	(	PUNCT
iajs-3245	44	13	mle	mle	PROPN
iajs-3245	44	14	)	)	PUNCT
iajs-3245	44	15	for	for	ADP
iajs-3245	44	16	the	the	DET
iajs-3245	44	17	parameters	parameter	NOUN
iajs-3245	44	18	,	,	PUNCT
iajs-3245	44	19	{	{	PUNCT
iajs-3245	44	20	𝛽0	𝛽0	NOUN
iajs-3245	44	21	,	,	PUNCT
iajs-3245	44	22	𝛽1	𝛽1	NOUN
iajs-3245	44	23	}	}	PUNCT
iajs-3245	44	24	,	,	PUNCT
iajs-3245	44	25	is	be	AUX
iajs-3245	44	26	obtained	obtain	VERB
iajs-3245	44	27	by	by	ADP
iajs-3245	44	28	finding	find	VERB
iajs-3245	44	29	(	(	PUNCT
iajs-3245	44	30	𝛽0̂	𝛽0̂	PROPN
iajs-3245	44	31	,	,	PUNCT
iajs-3245	44	32	𝛽1̂	𝛽1̂	NOUN
iajs-3245	44	33	)	)	PUNCT
iajs-3245	44	34	that	that	PRON
iajs-3245	44	35	maximizes	maximize	VERB
iajs-3245	44	36	:	:	PUNCT
iajs-3245	44	37	𝐿(𝛽0	𝐿(𝛽0	ADJ
iajs-3245	44	38	,	,	PUNCT
iajs-3245	44	39	𝛽1	𝛽1	NOUN
iajs-3245	44	40	)	)	PUNCT
iajs-3245	44	41	=	=	SYM
iajs-3245	44	42	∏	∏	PROPN
iajs-3245	44	43	𝑝𝑖	𝑝𝑖	X
iajs-3245	44	44	𝑦𝑖(1	𝑦𝑖(1	PROPN
iajs-3245	44	45	−	−	PROPN
iajs-3245	44	46	𝑝𝑖)𝑛𝑖−𝑦𝑖	𝑝𝑖)𝑛𝑖−𝑦𝑖	PROPN
iajs-3245	44	47	=	=	NOUN
iajs-3245	44	48	𝑛	𝑛	PRON
iajs-3245	44	49	𝑖=1	𝑖=1	PROPN
iajs-3245	44	50	∏	∏	NUM
iajs-3245	44	51	𝑒{𝑦𝑖(𝛽0+𝛽1𝑥𝑖	𝑒{𝑦𝑖(𝛽0+𝛽1𝑥𝑖	NOUN
iajs-3245	44	52	)	)	PUNCT
iajs-3245	44	53	}	}	PUNCT
iajs-3245	44	54	1+𝑒𝛽0+𝛽1𝑥𝑖	1+𝑒𝛽0+𝛽1𝑥𝑖	NUM
iajs-3245	45	1	𝑛	𝑛	DET
iajs-3245	45	2	𝑖=1	𝑖=1	PROPN
iajs-3245	45	3	(	(	PUNCT
iajs-3245	45	4	3	3	NUM
iajs-3245	45	5	)	)	PUNCT
iajs-3245	45	6	3	3	NUM
iajs-3245	45	7	.	.	X
iajs-3245	45	8	bayesian	bayesian	NOUN
iajs-3245	45	9	formulation	formulation	NOUN
iajs-3245	45	10	the	the	DET
iajs-3245	45	11	bayesian	bayesian	NOUN
iajs-3245	45	12	model	model	NOUN
iajs-3245	45	13	can	can	AUX
iajs-3245	45	14	be	be	AUX
iajs-3245	45	15	treated	treat	VERB
iajs-3245	45	16	like	like	ADP
iajs-3245	45	17	a	a	DET
iajs-3245	45	18	classification	classification	NOUN
iajs-3245	45	19	problem	problem	NOUN
iajs-3245	45	20	.	.	PUNCT
iajs-3245	46	1	researchers	researcher	NOUN
iajs-3245	46	2	can	can	AUX
iajs-3245	46	3	infer	infer	VERB
iajs-3245	46	4	the	the	DET
iajs-3245	46	5	individual	individual	NOUN
iajs-3245	46	6	from	from	ADP
iajs-3245	46	7	the	the	DET
iajs-3245	46	8	model	model	NOUN
iajs-3245	46	9	parameters	parameter	NOUN
iajs-3245	46	10	and	and	CCONJ
iajs-3245	46	11	the	the	DET
iajs-3245	46	12	data	datum	NOUN
iajs-3245	46	13	.	.	PUNCT
iajs-3245	47	1	from	from	ADP
iajs-3245	47	2	a	a	DET
iajs-3245	47	3	set	set	NOUN
iajs-3245	47	4	of	of	ADP
iajs-3245	47	5	likely	likely	ADJ
iajs-3245	47	6	divergent	divergent	ADJ
iajs-3245	47	7	opinions	opinion	NOUN
iajs-3245	47	8	about	about	ADP
iajs-3245	47	9	a	a	DET
iajs-3245	47	10	particular	particular	ADJ
iajs-3245	47	11	condition	condition	NOUN
iajs-3245	47	12	,	,	PUNCT
iajs-3245	47	13	one	one	NUM
iajs-3245	47	14	of	of	ADP
iajs-3245	47	15	two	two	NUM
iajs-3245	47	16	outcomes	outcome	NOUN
iajs-3245	47	17	can	can	AUX
iajs-3245	47	18	be	be	AUX
iajs-3245	47	19	inferred	infer	VERB
iajs-3245	47	20	:	:	PUNCT
iajs-3245	47	21	does	do	AUX
iajs-3245	47	22	individual	individual	PROPN
iajs-3245	47	23	j	j	PROPN
iajs-3245	47	24	actually	actually	ADV
iajs-3245	47	25	have	have	VERB
iajs-3245	47	26	a	a	DET
iajs-3245	47	27	particular	particular	ADJ
iajs-3245	47	28	disease	disease	NOUN
iajs-3245	47	29	state	state	NOUN
iajs-3245	47	30	(	(	PUNCT
iajs-3245	47	31	y	y	NOUN
iajs-3245	47	32	=	=	SYM
iajs-3245	47	33	0	0	PUNCT
iajs-3245	48	1	if	if	SCONJ
iajs-3245	48	2	no	no	PRON
iajs-3245	48	3	and	and	CCONJ
iajs-3245	48	4	y	y	NOUN
iajs-3245	48	5	=	=	NOUN
iajs-3245	48	6	1	1	NUM
iajs-3245	48	7	if	if	SCONJ
iajs-3245	48	8	yes	yes	INTJ
iajs-3245	48	9	)	)	PUNCT
iajs-3245	49	1	[	[	X
iajs-3245	49	2	13	13	NUM
iajs-3245	49	3	,	,	PUNCT
iajs-3245	49	4	14	14	NUM
iajs-3245	49	5	]	]	PUNCT
iajs-3245	49	6	.	.	PUNCT
iajs-3245	50	1	the	the	DET
iajs-3245	50	2	bayesian	bayesian	NOUN
iajs-3245	50	3	hierarchical	hierarchical	ADJ
iajs-3245	50	4	logistic	logistic	ADJ
iajs-3245	50	5	regression	regression	NOUN
iajs-3245	50	6	model	model	NOUN
iajs-3245	50	7	addresses	address	NOUN
iajs-3245	50	8	this	this	DET
iajs-3245	50	9	problem	problem	NOUN
iajs-3245	50	10	to	to	PART
iajs-3245	50	11	account	account	VERB
iajs-3245	50	12	for	for	ADP
iajs-3245	50	13	the	the	DET
iajs-3245	50	14	variability	variability	NOUN
iajs-3245	50	15	of	of	ADP
iajs-3245	50	16	outcomes	outcome	NOUN
iajs-3245	50	17	stemming	stem	VERB
iajs-3245	50	18	from	from	ADP
iajs-3245	50	19	both	both	DET
iajs-3245	50	20	informants	informant	NOUN
iajs-3245	50	21	and	and	CCONJ
iajs-3245	50	22	informative	informative	ADJ
iajs-3245	50	23	priorities	priority	NOUN
iajs-3245	50	24	.	.	PUNCT
iajs-3245	51	1	bayes	bayes	PROPN
iajs-3245	51	2	'	'	PART
iajs-3245	51	3	theorem	theorem	NOUN
iajs-3245	51	4	and	and	CCONJ
iajs-3245	51	5	a	a	DET
iajs-3245	51	6	generative	generative	ADJ
iajs-3245	51	7	model	model	NOUN
iajs-3245	51	8	can	can	AUX
iajs-3245	51	9	be	be	AUX
iajs-3245	51	10	applied	apply	VERB
iajs-3245	51	11	when	when	SCONJ
iajs-3245	51	12	we	we	PRON
iajs-3245	51	13	have	have	VERB
iajs-3245	51	14	data	datum	NOUN
iajs-3245	51	15	to	to	PART
iajs-3245	51	16	calculate	calculate	VERB
iajs-3245	51	17	the	the	DET
iajs-3245	51	18	posterior	posterior	ADJ
iajs-3245	51	19	probability	probability	NOUN
iajs-3245	51	20	distribution	distribution	NOUN
iajs-3245	51	21	of	of	ADP
iajs-3245	51	22	the	the	DET
iajs-3245	51	23	model	model	NOUN
iajs-3245	51	24	parameters	parameter	NOUN
iajs-3245	51	25	using	use	VERB
iajs-3245	51	26	the	the	DET
iajs-3245	51	27	prior	prior	ADJ
iajs-3245	51	28	distribution	distribution	NOUN
iajs-3245	51	29	as	as	ADP
iajs-3245	51	30	a	a	DET
iajs-3245	51	31	function	function	NOUN
iajs-3245	51	32	of	of	ADP
iajs-3245	51	33	the	the	DET
iajs-3245	51	34	predictors	predictor	NOUN
iajs-3245	51	35	(	(	PUNCT
iajs-3245	51	36	x	x	X
iajs-3245	51	37	)	)	PUNCT
iajs-3245	51	38	and	and	CCONJ
iajs-3245	51	39	the	the	DET
iajs-3245	51	40	response	response	NOUN
iajs-3245	51	41	(	(	PUNCT
iajs-3245	51	42	y	y	NOUN
iajs-3245	51	43	)	)	PUNCT
iajs-3245	51	44	.	.	PUNCT
iajs-3245	52	1	in	in	ADP
iajs-3245	52	2	general	general	ADJ
iajs-3245	52	3	,	,	PUNCT
iajs-3245	52	4	the	the	DET
iajs-3245	52	5	three	three	NUM
iajs-3245	52	6	critical	critical	ADJ
iajs-3245	52	7	components	component	NOUN
iajs-3245	52	8	associated	associate	VERB
iajs-3245	52	9	with	with	ADP
iajs-3245	52	10	parameter	parameter	NOUN
iajs-3245	52	11	estimation	estimation	NOUN
iajs-3245	52	12	in	in	ADP
iajs-3245	52	13	the	the	DET
iajs-3245	52	14	bayesian	bayesian	NOUN
iajs-3245	52	15	system	system	NOUN
iajs-3245	52	16	are	be	AUX
iajs-3245	52	17	the	the	DET
iajs-3245	52	18	prior	prior	ADJ
iajs-3245	52	19	distribution	distribution	NOUN
iajs-3245	52	20	,	,	PUNCT
iajs-3245	52	21	the	the	DET
iajs-3245	52	22	likelihood	likelihood	NOUN
iajs-3245	52	23	function	function	NOUN
iajs-3245	52	24	,	,	PUNCT
iajs-3245	52	25	and	and	CCONJ
iajs-3245	52	26	the	the	DET
iajs-3245	52	27	posterior	posterior	ADJ
iajs-3245	52	28	distribution	distribution	NOUN
iajs-3245	52	29	.	.	PUNCT
iajs-3245	53	1	bayes	bayes	PROPN
iajs-3245	53	2	'	'	PART
iajs-3245	53	3	theorem	theorem	NOUN
iajs-3245	53	4	formally	formally	ADV
iajs-3245	53	5	combines	combine	VERB
iajs-3245	53	6	these	these	DET
iajs-3245	53	7	three	three	NUM
iajs-3245	53	8	elements	element	NOUN
iajs-3245	53	9	:	:	PUNCT
iajs-3245	53	10	𝑃𝑜𝑠𝑡𝑒𝑟𝑖𝑜𝑟	𝑃𝑜𝑠𝑡𝑒𝑟𝑖𝑜𝑟	PROPN
iajs-3245	53	11	=	=	PUNCT
iajs-3245	53	12	𝐿𝑖𝑘𝑒𝑙𝑖ℎ𝑜𝑜𝑑	𝐿𝑖𝑘𝑒𝑙𝑖ℎ𝑜𝑜𝑑	PROPN
iajs-3245	53	13	×	×	PROPN
iajs-3245	53	14	𝑃𝑟𝑖𝑜𝑟	𝑃𝑟𝑖𝑜𝑟	PROPN
iajs-3245	53	15	ihjpas	ihjpa	VERB
iajs-3245	53	16	.	.	PUNCT
iajs-3245	54	1	2024	2024	NUM
iajs-3245	54	2	,	,	PUNCT
iajs-3245	54	3	37(4	37(4	PRON
iajs-3245	54	4	)	)	PUNCT
iajs-3245	54	5	394	394	NUM
iajs-3245	54	6	basically	basically	ADV
iajs-3245	54	7	,	,	PUNCT
iajs-3245	54	8	the	the	DET
iajs-3245	54	9	above	above	ADJ
iajs-3245	54	10	expression	expression	NOUN
iajs-3245	54	11	means	mean	VERB
iajs-3245	54	12	that	that	SCONJ
iajs-3245	54	13	the	the	DET
iajs-3245	54	14	knowledge	knowledge	NOUN
iajs-3245	54	15	in	in	ADP
iajs-3245	54	16	the	the	DET
iajs-3245	54	17	sample	sample	NOUN
iajs-3245	54	18	(	(	PUNCT
iajs-3245	54	19	reflected	reflect	VERB
iajs-3245	54	20	in	in	ADP
iajs-3245	54	21	the	the	DET
iajs-3245	54	22	likelihood	likelihood	NOUN
iajs-3245	54	23	function	function	NOUN
iajs-3245	54	24	)	)	PUNCT
iajs-3245	54	25	is	be	AUX
iajs-3245	54	26	combined	combine	VERB
iajs-3245	54	27	with	with	ADP
iajs-3245	54	28	data	datum	NOUN
iajs-3245	54	29	from	from	ADP
iajs-3245	54	30	other	other	ADJ
iajs-3245	54	31	sources	source	NOUN
iajs-3245	54	32	(	(	PUNCT
iajs-3245	54	33	summarised	summarise	VERB
iajs-3245	54	34	in	in	ADP
iajs-3245	54	35	the	the	DET
iajs-3245	54	36	prior	prior	ADJ
iajs-3245	54	37	distribution	distribution	NOUN
iajs-3245	54	38	)	)	PUNCT
iajs-3245	54	39	to	to	PART
iajs-3245	54	40	obtain	obtain	VERB
iajs-3245	54	41	the	the	DET
iajs-3245	54	42	posterior	posterior	ADJ
iajs-3245	54	43	distribution	distribution	NOUN
iajs-3245	54	44	.	.	PUNCT
iajs-3245	55	1	all	all	DET
iajs-3245	55	2	available	available	ADJ
iajs-3245	55	3	information	information	NOUN
iajs-3245	55	4	about	about	ADP
iajs-3245	55	5	the	the	DET
iajs-3245	55	6	parameters	parameter	NOUN
iajs-3245	55	7	of	of	ADP
iajs-3245	55	8	the	the	DET
iajs-3245	55	9	model	model	NOUN
iajs-3245	55	10	is	be	AUX
iajs-3245	55	11	incorporated	incorporate	VERB
iajs-3245	55	12	into	into	ADP
iajs-3245	55	13	the	the	DET
iajs-3245	55	14	posterior	posterior	ADJ
iajs-3245	55	15	distribution	distribution	NOUN
iajs-3245	55	16	.	.	PUNCT
iajs-3245	56	1	[	[	X
iajs-3245	56	2	15],[16	15],[16	X
iajs-3245	56	3	]	]	X
iajs-3245	56	4	deals	deal	NOUN
iajs-3245	56	5	in	in	ADP
iajs-3245	56	6	detail	detail	NOUN
iajs-3245	56	7	with	with	ADP
iajs-3245	56	8	the	the	DET
iajs-3245	56	9	principle	principle	NOUN
iajs-3245	56	10	of	of	ADP
iajs-3245	56	11	bayesian	bayesian	NOUN
iajs-3245	56	12	analysis	analysis	NOUN
iajs-3245	56	13	.	.	PUNCT
iajs-3245	57	1	the	the	DET
iajs-3245	57	2	likelihood	likelihood	NOUN
iajs-3245	57	3	contribution	contribution	NOUN
iajs-3245	57	4	from	from	ADP
iajs-3245	57	5	the	the	DET
iajs-3245	57	6	𝑖𝑡ℎ	𝑖𝑡ℎ	NOUN
iajs-3245	57	7	individual	individual	NOUN
iajs-3245	57	8	is	be	AUX
iajs-3245	57	9	binomial	binomial	ADJ
iajs-3245	57	10	,	,	PUNCT
iajs-3245	57	11	𝐿𝑖	𝐿𝑖	NOUN
iajs-3245	57	12	=	=	PUNCT
iajs-3245	57	13	(	(	PUNCT
iajs-3245	57	14	𝑒𝛽0+𝛽1𝑥𝑖	𝑒𝛽0+𝛽1𝑥𝑖	ADJ
iajs-3245	57	15	1	1	NUM
iajs-3245	57	16	+	+	NUM
iajs-3245	57	17	𝑒𝛽0+𝛽1𝑥𝑖	𝑒𝛽0+𝛽1𝑥𝑖	ADJ
iajs-3245	57	18	)	)	PUNCT
iajs-3245	57	19	𝑦𝑖	𝑦𝑖	NUM
iajs-3245	57	20	×	×	NOUN
iajs-3245	57	21	(	(	PUNCT
iajs-3245	57	22	1	1	NUM
iajs-3245	57	23	−	−	NOUN
iajs-3245	57	24	𝑒𝛽0+𝛽1𝑥𝑖	𝑒𝛽0+𝛽1𝑥𝑖	ADJ
iajs-3245	57	25	1	1	NUM
iajs-3245	57	26	+	+	NUM
iajs-3245	57	27	𝑒𝛽0+𝛽1𝑥𝑖	𝑒𝛽0+𝛽1𝑥𝑖	ADJ
iajs-3245	57	28	)	)	PUNCT
iajs-3245	57	29	(	(	PUNCT
iajs-3245	57	30	1−𝑦𝑖	1−𝑦𝑖	NOUN
iajs-3245	57	31	)	)	PUNCT
iajs-3245	57	32	(	(	PUNCT
iajs-3245	57	33	4	4	X
iajs-3245	57	34	)	)	PUNCT
iajs-3245	57	35	since	since	SCONJ
iajs-3245	57	36	individual	individual	ADJ
iajs-3245	57	37	subjects	subject	NOUN
iajs-3245	57	38	are	be	AUX
iajs-3245	57	39	presumed	presume	VERB
iajs-3245	57	40	to	to	PART
iajs-3245	57	41	be	be	AUX
iajs-3245	57	42	separate	separate	ADJ
iajs-3245	57	43	from	from	ADP
iajs-3245	57	44	one	one	NUM
iajs-3245	57	45	another	another	DET
iajs-3245	57	46	,	,	PUNCT
iajs-3245	57	47	the	the	DET
iajs-3245	57	48	probability	probability	NOUN
iajs-3245	57	49	function	function	NOUN
iajs-3245	57	50	for	for	ADP
iajs-3245	57	51	a	a	DET
iajs-3245	57	52	data	datum	NOUN
iajs-3245	57	53	set	set	VERB
iajs-3245	57	54	of	of	ADP
iajs-3245	57	55	𝑛	𝑛	DET
iajs-3245	57	56	subjects	subject	NOUN
iajs-3245	57	57	is	be	AUX
iajs-3245	57	58	then	then	ADV
iajs-3245	57	59	the	the	DET
iajs-3245	57	60	probability	probability	NOUN
iajs-3245	57	61	distribution	distribution	NOUN
iajs-3245	57	62	.	.	PUNCT
iajs-3245	58	1	𝐿	𝐿	PROPN
iajs-3245	58	2	=	=	SYM
iajs-3245	58	3	∏	∏	PROPN
iajs-3245	58	4	(	(	PUNCT
iajs-3245	58	5	𝑒𝛽0+𝛽1𝑥𝑖	𝑒𝛽0+𝛽1𝑥𝑖	ADJ
iajs-3245	58	6	1	1	NUM
iajs-3245	58	7	+	+	NUM
iajs-3245	58	8	𝑒𝛽0+𝛽1𝑥𝑖	𝑒𝛽0+𝛽1𝑥𝑖	ADJ
iajs-3245	58	9	)	)	PUNCT
iajs-3245	58	10	𝑦𝑖	𝑦𝑖	NUM
iajs-3245	58	11	×	×	NOUN
iajs-3245	58	12	(	(	PUNCT
iajs-3245	58	13	1	1	NUM
iajs-3245	58	14	−	−	NOUN
iajs-3245	58	15	𝑒𝛽0+𝛽1𝑥𝑖	𝑒𝛽0+𝛽1𝑥𝑖	ADJ
iajs-3245	58	16	1	1	NUM
iajs-3245	58	17	+	+	NUM
iajs-3245	58	18	𝑒𝛽0+𝛽1𝑥𝑖	𝑒𝛽0+𝛽1𝑥𝑖	ADJ
iajs-3245	58	19	)	)	PUNCT
iajs-3245	58	20	(	(	PUNCT
iajs-3245	58	21	1−𝑦𝑖	1−𝑦𝑖	NUM
iajs-3245	58	22	)	)	PUNCT
iajs-3245	58	23	𝑛	𝑛	PROPN
iajs-3245	58	24	𝑖=1	𝑖=1	PROPN
iajs-3245	58	25	(	(	PUNCT
iajs-3245	58	26	5	5	NUM
iajs-3245	58	27	)	)	PUNCT
iajs-3245	58	28	by	by	ADP
iajs-3245	58	29	assuming	assume	VERB
iajs-3245	58	30	the	the	DET
iajs-3245	58	31	multivariate	multivariate	NOUN
iajs-3245	58	32	normal	normal	ADJ
iajs-3245	58	33	prior	prior	ADV
iajs-3245	58	34	on	on	ADP
iajs-3245	58	35	𝛽	𝛽	NOUN
iajs-3245	58	36	;	;	PUNCT
iajs-3245	58	37	i.e.	i.e.	X
iajs-3245	58	38	𝛽𝑖	𝛽𝑖	ADJ
iajs-3245	58	39	∼	∼	NOUN
iajs-3245	58	40	𝑁(𝜇𝑖	𝑁(𝜇𝑖	NOUN
iajs-3245	58	41	,	,	PUNCT
iajs-3245	58	42	𝜎𝑖	𝜎𝑖	DET
iajs-3245	58	43	2	2	NUM
iajs-3245	58	44	)	)	PUNCT
iajs-3245	58	45	,	,	PUNCT
iajs-3245	58	46	we	we	PRON
iajs-3245	58	47	get	get	VERB
iajs-3245	58	48	𝑓(𝛽	𝑓(𝛽	ADJ
iajs-3245	58	49	)	)	PUNCT
iajs-3245	58	50	=	=	SYM
iajs-3245	58	51	1	1	NUM
iajs-3245	58	52	√2𝜋𝜎2	√2𝜋𝜎2	VERB
iajs-3245	58	53	𝑒	𝑒	PROPN
iajs-3245	58	54	{	{	PUNCT
iajs-3245	58	55	−	−	PROPN
iajs-3245	58	56	1	1	NUM
iajs-3245	58	57	2	2	NUM
iajs-3245	58	58	(	(	PUNCT
iajs-3245	58	59	𝛽𝑖−𝜇𝑖	𝛽𝑖−𝜇𝑖	ADV
iajs-3245	58	60	𝜎𝑖	𝜎𝑖	X
iajs-3245	58	61	)	)	PUNCT
iajs-3245	58	62	2	2	NUM
iajs-3245	58	63	}	}	PUNCT
iajs-3245	58	64	(	(	PUNCT
iajs-3245	58	65	6	6	NUM
iajs-3245	58	66	)	)	PUNCT
iajs-3245	58	67	as	as	ADP
iajs-3245	58	68	a	a	DET
iajs-3245	58	69	result	result	NOUN
iajs-3245	58	70	,	,	PUNCT
iajs-3245	58	71	the	the	DET
iajs-3245	58	72	posterior	posterior	ADJ
iajs-3245	58	73	distribution	distribution	NOUN
iajs-3245	58	74	is	be	AUX
iajs-3245	58	75	calculated	calculate	VERB
iajs-3245	58	76	by	by	ADP
iajs-3245	58	77	multiplying	multiply	VERB
iajs-3245	58	78	the	the	DET
iajs-3245	58	79	probability	probability	NOUN
iajs-3245	58	80	function	function	NOUN
iajs-3245	58	81	by	by	ADP
iajs-3245	58	82	the	the	DET
iajs-3245	58	83	prior	prior	ADJ
iajs-3245	58	84	:	:	PUNCT
iajs-3245	58	85	posterior	posterior	ADJ
iajs-3245	58	86	=	=	SYM
iajs-3245	58	87	∏	∏	X
iajs-3245	58	88	(	(	PUNCT
iajs-3245	58	89	eβ0+β1xi	eβ0+β1xi	PROPN
iajs-3245	58	90	1	1	NUM
iajs-3245	58	91	+	+	PROPN
iajs-3245	58	92	eβ0+β1xi	eβ0+β1xi	NOUN
iajs-3245	58	93	)	)	PUNCT
iajs-3245	58	94	yi	yi	PROPN
iajs-3245	58	95	×	×	NOUN
iajs-3245	58	96	(	(	PUNCT
iajs-3245	58	97	1	1	NUM
iajs-3245	58	98	−	−	PROPN
iajs-3245	58	99	eβ0+β1xi	eβ0+β1xi	PROPN
iajs-3245	58	100	1	1	NUM
iajs-3245	58	101	+	+	PROPN
iajs-3245	58	102	eβ0+β1xi	eβ0+β1xi	X
iajs-3245	58	103	)	)	PUNCT
iajs-3245	58	104	(	(	PUNCT
iajs-3245	58	105	1−yi	1−yi	NUM
iajs-3245	58	106	)	)	PUNCT
iajs-3245	58	107	n	n	CCONJ
iajs-3245	58	108	i=1	i=1	PROPN
iajs-3245	58	109	×	×	NOUN
iajs-3245	58	110	1	1	NUM
iajs-3245	58	111	√2πσ2	√2πσ2	PROPN
iajs-3245	58	112	e	e	NOUN
iajs-3245	58	113	{	{	PUNCT
iajs-3245	58	114	−	−	PROPN
iajs-3245	58	115	1	1	NUM
iajs-3245	58	116	2	2	NUM
iajs-3245	58	117	(	(	PUNCT
iajs-3245	58	118	βi−μi	βi−μi	X
iajs-3245	58	119	σi	σi	NOUN
iajs-3245	58	120	)	)	PUNCT
iajs-3245	58	121	2	2	NUM
iajs-3245	58	122	}	}	PUNCT
iajs-3245	58	123	(	(	PUNCT
iajs-3245	58	124	7	7	X
iajs-3245	58	125	)	)	PUNCT
iajs-3245	58	126	we	we	PRON
iajs-3245	58	127	can	can	AUX
iajs-3245	58	128	use	use	VERB
iajs-3245	58	129	the	the	DET
iajs-3245	58	130	metropolis	metropolis	PROPN
iajs-3245	58	131	-	-	PUNCT
iajs-3245	58	132	hasting	hasting	PROPN
iajs-3245	58	133	procedure	procedure	NOUN
iajs-3245	58	134	to	to	ADP
iajs-3245	58	135	sample	sample	NOUN
iajs-3245	58	136	from	from	ADP
iajs-3245	58	137	the	the	DET
iajs-3245	58	138	above	above	ADJ
iajs-3245	58	139	posterior	posterior	ADJ
iajs-3245	58	140	distribution	distribution	NOUN
iajs-3245	58	141	.	.	PUNCT
iajs-3245	59	1	the	the	DET
iajs-3245	59	2	following	follow	VERB
iajs-3245	59	3	section	section	NOUN
iajs-3245	59	4	will	will	AUX
iajs-3245	59	5	present	present	VERB
iajs-3245	59	6	the	the	DET
iajs-3245	59	7	algorithm	algorithm	NOUN
iajs-3245	59	8	in	in	ADP
iajs-3245	59	9	detail	detail	NOUN
iajs-3245	59	10	.	.	PUNCT
iajs-3245	60	1	3.1	3.1	NUM
iajs-3245	60	2	markov	markov	NOUN
iajs-3245	60	3	chain	chain	NOUN
iajs-3245	60	4	monte	monte	PROPN
iajs-3245	60	5	carlo	carlo	PROPN
iajs-3245	60	6	if	if	SCONJ
iajs-3245	60	7	a	a	DET
iajs-3245	60	8	sequence	sequence	NOUN
iajs-3245	60	9	of	of	ADP
iajs-3245	60	10	numbers	number	NOUN
iajs-3245	60	11	follows	follow	VERB
iajs-3245	60	12	the	the	DET
iajs-3245	60	13	below	below	ADJ
iajs-3245	60	14	graphical	graphical	ADJ
iajs-3245	60	15	model	model	NOUN
iajs-3245	60	16	,	,	PUNCT
iajs-3245	60	17	it	it	PRON
iajs-3245	60	18	is	be	AUX
iajs-3245	60	19	a	a	DET
iajs-3245	60	20	markov	markov	NOUN
iajs-3245	60	21	chain	chain	NOUN
iajs-3245	60	22	is	be	AUX
iajs-3245	60	23	𝑃(𝑋5|𝑋4	𝑃(𝑋5|𝑋4	NOUN
iajs-3245	60	24	,	,	PUNCT
iajs-3245	60	25	𝑋3	𝑋3	NOUN
iajs-3245	60	26	,	,	PUNCT
iajs-3245	60	27	𝑋2	𝑋2	VERB
iajs-3245	60	28	,	,	PUNCT
iajs-3245	60	29	𝑋1	𝑋1	PROPN
iajs-3245	60	30	)	)	PUNCT
iajs-3245	60	31	=	=	PUNCT
iajs-3245	60	32	𝑃(𝑋5|𝑋4	𝑃(𝑋5|𝑋4	NOUN
iajs-3245	60	33	)	)	PUNCT
iajs-3245	60	34	.	.	PUNCT
iajs-3245	61	1	as	as	ADP
iajs-3245	61	2	a	a	DET
iajs-3245	61	3	result	result	NOUN
iajs-3245	61	4	,	,	PUNCT
iajs-3245	61	5	the	the	DET
iajs-3245	61	6	probability	probability	NOUN
iajs-3245	61	7	of	of	ADP
iajs-3245	61	8	reaching	reach	VERB
iajs-3245	61	9	a	a	DET
iajs-3245	61	10	specific	specific	ADJ
iajs-3245	61	11	state	state	NOUN
iajs-3245	61	12	is	be	AUX
iajs-3245	61	13	solely	solely	ADV
iajs-3245	61	14	determined	determine	VERB
iajs-3245	61	15	by	by	ADP
iajs-3245	61	16	the	the	DET
iajs-3245	61	17	chain	chain	NOUN
iajs-3245	61	18	's	's	PART
iajs-3245	61	19	previous	previous	ADJ
iajs-3245	61	20	state	state	NOUN
iajs-3245	61	21	[	[	X
iajs-3245	61	22	10	10	NUM
iajs-3245	61	23	]	]	PUNCT
iajs-3245	61	24	.	.	PUNCT
iajs-3245	62	1	using	use	VERB
iajs-3245	62	2	the	the	DET
iajs-3245	62	3	full	full	ADJ
iajs-3245	62	4	joint	joint	ADJ
iajs-3245	62	5	density	density	NOUN
iajs-3245	62	6	function	function	NOUN
iajs-3245	62	7	,	,	PUNCT
iajs-3245	62	8	the	the	DET
iajs-3245	62	9	metropolis	metropolis	PROPN
iajs-3245	62	10	-	-	PUNCT
iajs-3245	62	11	hasting	hasting	PROPN
iajs-3245	62	12	(	(	PUNCT
iajs-3245	62	13	mh	mh	PROPN
iajs-3245	62	14	)	)	PUNCT
iajs-3245	62	15	algorithm	algorithm	NOUN
iajs-3245	62	16	can	can	AUX
iajs-3245	62	17	be	be	AUX
iajs-3245	62	18	used	use	VERB
iajs-3245	62	19	.	.	PUNCT
iajs-3245	63	1	the	the	DET
iajs-3245	63	2	mh	mh	PROPN
iajs-3245	63	3	technique	technique	NOUN
iajs-3245	63	4	is	be	AUX
iajs-3245	63	5	an	an	DET
iajs-3245	63	6	iterative	iterative	NOUN
iajs-3245	63	7	method	method	NOUN
iajs-3245	63	8	that	that	PRON
iajs-3245	63	9	generates	generate	VERB
iajs-3245	63	10	a	a	DET
iajs-3245	63	11	markov	markov	NOUN
iajs-3245	63	12	chain	chain	NOUN
iajs-3245	63	13	sequence	sequence	NOUN
iajs-3245	63	14	to	to	PART
iajs-3245	63	15	estimate	estimate	VERB
iajs-3245	63	16	the	the	DET
iajs-3245	63	17	posterior	posterior	ADJ
iajs-3245	63	18	distribution	distribution	NOUN
iajs-3245	63	19	's	's	PART
iajs-3245	63	20	parameters	parameter	NOUN
iajs-3245	63	21	[	[	X
iajs-3245	63	22	17,18	17,18	X
iajs-3245	63	23	]	]	PUNCT
iajs-3245	63	24	.	.	PUNCT
iajs-3245	64	1	the	the	DET
iajs-3245	64	2	following	follow	VERB
iajs-3245	64	3	steps	step	NOUN
iajs-3245	64	4	summarize	summarize	VERB
iajs-3245	64	5	the	the	DET
iajs-3245	64	6	metropolis	metropolis	PROPN
iajs-3245	64	7	-	-	PUNCT
iajs-3245	64	8	hasting	hasting	PROPN
iajs-3245	64	9	algorithm	algorithm	NOUN
iajs-3245	64	10	[	[	X
iajs-3245	64	11	19	19	NUM
iajs-3245	64	12	,	,	PUNCT
iajs-3245	64	13	20	20	NUM
iajs-3245	64	14	]	]	PUNCT
iajs-3245	64	15	:	:	PUNCT
iajs-3245	64	16	metropolis	metropolis	PROPN
iajs-3245	64	17	-	-	PUNCT
iajs-3245	64	18	hasting	hasting	PROPN
iajs-3245	64	19	algorithm	algorithm	PROPN
iajs-3245	64	20	1	1	NUM
iajs-3245	64	21	.	.	PUNCT
iajs-3245	64	22	initialize	initialize	VERB
iajs-3245	64	23	𝑥(0	𝑥(0	PROPN
iajs-3245	64	24	)	)	PUNCT
iajs-3245	64	25	2	2	NUM
iajs-3245	64	26	.	.	X
iajs-3245	65	1	for	for	ADP
iajs-3245	65	2	i	i	PRON
iajs-3245	65	3	=	=	NOUN
iajs-3245	65	4	0	0	NUM
iajs-3245	65	5	to	to	ADP
iajs-3245	65	6	k−1	k−1	PROPN
iajs-3245	65	7			NOUN
iajs-3245	65	8	calculate	calculate	VERB
iajs-3245	65	9	𝑢	𝑢	PRON
iajs-3245	65	10	∼	∼	NOUN
iajs-3245	65	11	uniform	uniform	NOUN
iajs-3245	65	12	distribution[0,1	distribution[0,1	ADP
iajs-3245	65	13	]	]	PUNCT
iajs-3245	65	14			NOUN
iajs-3245	65	15	calculate	calculate	VERB
iajs-3245	65	16	𝑋⋆	𝑋⋆	PRON
iajs-3245	65	17	∼	∼	NOUN
iajs-3245	65	18	𝑞(𝑋⋆|𝑋(𝑖	𝑞(𝑋⋆|𝑋(𝑖	NOUN
iajs-3245	65	19	)	)	PUNCT
iajs-3245	65	20	)	)	PUNCT
iajs-3245	65	21			PUNCT
iajs-3245	66	1	if	if	SCONJ
iajs-3245	66	2	𝑢	𝑢	X
iajs-3245	66	3	<	<	X
iajs-3245	66	4	𝑅(𝑋(𝑖	𝑅(𝑋(𝑖	X
iajs-3245	66	5	)	)	PUNCT
iajs-3245	66	6	,	,	PUNCT
iajs-3245	66	7	𝑋⋆	𝑋⋆	PROPN
iajs-3245	66	8	)	)	PUNCT
iajs-3245	66	9	=	=	SYM
iajs-3245	66	10	min{1	min{1	PROPN
iajs-3245	66	11	,	,	PUNCT
iajs-3245	66	12	𝐹(𝑋⋆)𝑞(𝑧(𝑖)|𝑍⋆	𝐹(𝑋⋆)𝑞(𝑧(𝑖)|𝑍⋆	PROPN
iajs-3245	66	13	)	)	PUNCT
iajs-3245	66	14	𝐹(𝑧(𝑖))𝑞(𝑋⋆|𝑍(𝑖	𝐹(𝑧(𝑖))𝑞(𝑋⋆|𝑍(𝑖	NUM
iajs-3245	66	15	)	)	PUNCT
iajs-3245	66	16	)	)	PUNCT
iajs-3245	66	17	}	}	PUNCT
iajs-3245	67	1			PRON
iajs-3245	67	2	𝑋(𝑖+1	𝑋(𝑖+1	NOUN
iajs-3245	67	3	)	)	PUNCT
iajs-3245	68	1	=	=	NOUN
iajs-3245	68	2	𝑋⋆	𝑋⋆	PRON
iajs-3245	68	3			NOUN
iajs-3245	68	4	𝑒𝑙𝑠𝑒	𝑒𝑙𝑠𝑒	VERB
iajs-3245	68	5			NOUN
iajs-3245	68	6	𝑋(𝑖+1	𝑋(𝑖+1	NOUN
iajs-3245	68	7	)	)	PUNCT
iajs-3245	68	8	=	=	SYM
iajs-3245	69	1	𝑋(𝑖	𝑋(𝑖	X
iajs-3245	69	2	)	)	PUNCT
iajs-3245	69	3	we	we	PRON
iajs-3245	69	4	can	can	AUX
iajs-3245	69	5	carefully	carefully	ADV
iajs-3245	69	6	choose	choose	VERB
iajs-3245	69	7	the	the	DET
iajs-3245	69	8	proposal	proposal	NOUN
iajs-3245	69	9	distribution	distribution	NOUN
iajs-3245	69	10	𝑞.	𝑞.	ADP
iajs-3245	69	11	the	the	DET
iajs-3245	69	12	mh	mh	PROPN
iajs-3245	69	13	algorithm	algorithm	PROPN
iajs-3245	69	14	assumes	assume	VERB
iajs-3245	69	15	a	a	DET
iajs-3245	69	16	symmetric	symmetric	ADJ
iajs-3245	69	17	random	random	ADJ
iajs-3245	69	18	walk	walk	NOUN
iajs-3245	69	19	for	for	ADP
iajs-3245	69	20	the	the	DET
iajs-3245	69	21	proposal	proposal	NOUN
iajs-3245	69	22	distribution	distribution	NOUN
iajs-3245	69	23	,	,	PUNCT
iajs-3245	69	24	i.e.	i.e.	X
iajs-3245	69	25	,	,	PUNCT
iajs-3245	69	26	𝑞(𝑥|𝑦	𝑞(𝑥|𝑦	VERB
iajs-3245	69	27	)	)	PUNCT
iajs-3245	69	28	=	=	PUNCT
iajs-3245	69	29	𝑞(𝑦|𝑥	𝑞(𝑦|𝑥	NUM
iajs-3245	69	30	)	)	PUNCT
iajs-3245	69	31	.	.	PUNCT
iajs-3245	70	1	𝑝(𝑥	𝑝(𝑥	X
iajs-3245	70	2	)	)	PUNCT
iajs-3245	70	3	does	do	AUX
iajs-3245	70	4	not	not	PART
iajs-3245	70	5	even	even	ADV
iajs-3245	70	6	have	have	VERB
iajs-3245	70	7	to	to	PART
iajs-3245	70	8	be	be	AUX
iajs-3245	70	9	ihjpas	ihjpa	NOUN
iajs-3245	70	10	.	.	PUNCT
iajs-3245	71	1	2024	2024	NUM
iajs-3245	71	2	,	,	PUNCT
iajs-3245	71	3	37(4	37(4	PRON
iajs-3245	71	4	)	)	PUNCT
iajs-3245	71	5	395	395	NUM
iajs-3245	71	6	the	the	DET
iajs-3245	71	7	full	full	ADJ
iajs-3245	71	8	bayesian	bayesian	NOUN
iajs-3245	71	9	probability	probability	NOUN
iajs-3245	71	10	but	but	CCONJ
iajs-3245	71	11	simply	simply	ADV
iajs-3245	71	12	is	be	AUX
iajs-3245	71	13	required	require	VERB
iajs-3245	71	14	to	to	PART
iajs-3245	71	15	be	be	AUX
iajs-3245	71	16	proportionate	proportionate	ADJ
iajs-3245	71	17	to	to	ADP
iajs-3245	71	18	it	it	PRON
iajs-3245	71	19	.	.	PUNCT
iajs-3245	72	1	this	this	PRON
iajs-3245	72	2	is	be	AUX
iajs-3245	72	3	clear	clear	ADJ
iajs-3245	72	4	since	since	SCONJ
iajs-3245	72	5	the	the	DET
iajs-3245	72	6	bayes	bayes	PROPN
iajs-3245	72	7	denominators	denominator	NOUN
iajs-3245	72	8	will	will	AUX
iajs-3245	72	9	cancel	cancel	VERB
iajs-3245	72	10	out	out	ADP
iajs-3245	72	11	.	.	PUNCT
iajs-3245	73	1	4	4	X
iajs-3245	73	2	.	.	X
iajs-3245	73	3	simulation	simulation	NOUN
iajs-3245	73	4	studies	study	VERB
iajs-3245	73	5	the	the	DET
iajs-3245	73	6	posterior	posterior	ADJ
iajs-3245	73	7	distributions	distribution	NOUN
iajs-3245	73	8	for	for	ADP
iajs-3245	73	9	the	the	DET
iajs-3245	73	10	parameters	parameter	NOUN
iajs-3245	73	11	are	be	AUX
iajs-3245	73	12	applied	apply	VERB
iajs-3245	73	13	in	in	ADP
iajs-3245	73	14	this	this	DET
iajs-3245	73	15	section	section	NOUN
iajs-3245	73	16	(	(	PUNCT
iajs-3245	73	17	eq	eq	NOUN
iajs-3245	73	18	.	.	PROPN
iajs-3245	73	19	1	1	NUM
iajs-3245	73	20	and	and	CCONJ
iajs-3245	73	21	eq	eq	NOUN
iajs-3245	73	22	.	.	PROPN
iajs-3245	73	23	2	2	NUM
iajs-3245	73	24	)	)	PUNCT
iajs-3245	73	25	.	.	PUNCT
iajs-3245	74	1	datasets	dataset	NOUN
iajs-3245	74	2	of	of	ADP
iajs-3245	74	3	size	size	NOUN
iajs-3245	74	4	100	100	NUM
iajs-3245	74	5	,	,	PUNCT
iajs-3245	74	6	200	200	NUM
iajs-3245	74	7	and	and	CCONJ
iajs-3245	74	8	400	400	NUM
iajs-3245	74	9	are	be	AUX
iajs-3245	74	10	simulated	simulate	VERB
iajs-3245	74	11	.	.	PUNCT
iajs-3245	75	1	first	first	ADV
iajs-3245	75	2	,	,	PUNCT
iajs-3245	75	3	a	a	DET
iajs-3245	75	4	set	set	NOUN
iajs-3245	75	5	of	of	ADP
iajs-3245	75	6	five	five	NUM
iajs-3245	75	7	variables	variable	NOUN
iajs-3245	75	8	are	be	AUX
iajs-3245	75	9	simulated	simulate	VERB
iajs-3245	75	10	uniformly	uniformly	ADV
iajs-3245	75	11	from	from	ADP
iajs-3245	75	12	a	a	DET
iajs-3245	75	13	uniform	uniform	ADJ
iajs-3245	75	14	distribution	distribution	NOUN
iajs-3245	75	15	u(0	u(0	PROPN
iajs-3245	75	16	1	1	NUM
iajs-3245	75	17	)	)	PUNCT
iajs-3245	75	18	,	,	PUNCT
iajs-3245	75	19	and	and	CCONJ
iajs-3245	75	20	then	then	ADV
iajs-3245	75	21	𝛽0	𝛽0	ADJ
iajs-3245	75	22	=	=	PUNCT
iajs-3245	75	23	2.41	2.41	NUM
iajs-3245	75	24	,	,	PUNCT
iajs-3245	75	25	𝛽1	𝛽1	NOUN
iajs-3245	75	26	=	=	SYM
iajs-3245	75	27	−1.11	−1.11	NOUN
iajs-3245	75	28	𝛽2	𝛽2	PROPN
iajs-3245	75	29	=	=	SYM
iajs-3245	75	30	4.72	4.72	NUM
iajs-3245	75	31	,	,	PUNCT
iajs-3245	75	32	𝛽3	𝛽3	NOUN
iajs-3245	75	33	=	=	PUNCT
iajs-3245	75	34	0.31	0.31	NUM
iajs-3245	75	35	,	,	PUNCT
iajs-3245	75	36	and	and	CCONJ
iajs-3245	75	37	𝛽4	𝛽4	PROPN
iajs-3245	75	38	=	=	NOUN
iajs-3245	75	39	4.10	4.10	NUM
iajs-3245	75	40	are	be	AUX
iajs-3245	75	41	used	use	VERB
iajs-3245	75	42	.	.	PUNCT
iajs-3245	76	1	by	by	ADP
iajs-3245	76	2	applying	apply	VERB
iajs-3245	76	3	the	the	DET
iajs-3245	76	4	mh	mh	PROPN
iajs-3245	76	5	algorithm	algorithm	NOUN
iajs-3245	76	6	for	for	ADP
iajs-3245	76	7	10000	10000	NUM
iajs-3245	76	8	repetitions	repetition	NOUN
iajs-3245	76	9	,	,	PUNCT
iajs-3245	76	10	the	the	DET
iajs-3245	76	11	parameters	parameter	NOUN
iajs-3245	76	12	were	be	AUX
iajs-3245	76	13	obtained	obtain	VERB
iajs-3245	76	14	by	by	ADP
iajs-3245	76	15	calculating	calculate	VERB
iajs-3245	76	16	the	the	DET
iajs-3245	76	17	posterior	posterior	ADJ
iajs-3245	76	18	sample	sample	NOUN
iajs-3245	76	19	mean	mean	VERB
iajs-3245	77	1	[	[	X
iajs-3245	77	2	21	21	NUM
iajs-3245	77	3	,	,	PUNCT
iajs-3245	77	4	22	22	NUM
iajs-3245	77	5	]	]	PUNCT
iajs-3245	77	6	.	.	PUNCT
iajs-3245	78	1	in	in	ADP
iajs-3245	78	2	figure	figure	NOUN
iajs-3245	78	3	1	1	NUM
iajs-3245	78	4	,	,	PUNCT
iajs-3245	78	5	the	the	DET
iajs-3245	78	6	posterior	posterior	ADJ
iajs-3245	78	7	samples	sample	NOUN
iajs-3245	78	8	are	be	AUX
iajs-3245	78	9	plotted	plot	VERB
iajs-3245	78	10	as	as	ADP
iajs-3245	78	11	histograms	histogram	NOUN
iajs-3245	78	12	,	,	PUNCT
iajs-3245	78	13	and	and	CCONJ
iajs-3245	78	14	a	a	DET
iajs-3245	78	15	red	red	ADJ
iajs-3245	78	16	line	line	NOUN
iajs-3245	78	17	remarks	remark	VERB
iajs-3245	78	18	the	the	DET
iajs-3245	78	19	true	true	ADJ
iajs-3245	78	20	values	value	NOUN
iajs-3245	78	21	for	for	ADP
iajs-3245	78	22	these	these	DET
iajs-3245	78	23	parameters	parameter	NOUN
iajs-3245	78	24	.	.	PUNCT
iajs-3245	79	1	obviously	obviously	ADV
iajs-3245	79	2	,	,	PUNCT
iajs-3245	79	3	the	the	DET
iajs-3245	79	4	histograms	histogram	NOUN
iajs-3245	79	5	are	be	AUX
iajs-3245	79	6	approximately	approximately	ADV
iajs-3245	79	7	normal	normal	ADJ
iajs-3245	79	8	,	,	PUNCT
iajs-3245	79	9	and	and	CCONJ
iajs-3245	79	10	the	the	DET
iajs-3245	79	11	true	true	ADJ
iajs-3245	79	12	values	value	NOUN
iajs-3245	79	13	are	be	AUX
iajs-3245	79	14	close	close	ADJ
iajs-3245	79	15	to	to	ADP
iajs-3245	79	16	the	the	DET
iajs-3245	79	17	sample	sample	NOUN
iajs-3245	79	18	's	's	PART
iajs-3245	79	19	mean	mean	ADJ
iajs-3245	79	20	.	.	PUNCT
iajs-3245	80	1	in	in	ADP
iajs-3245	80	2	addition	addition	NOUN
iajs-3245	80	3	,	,	PUNCT
iajs-3245	80	4	the	the	DET
iajs-3245	80	5	model	model	NOUN
iajs-3245	80	6	is	be	AUX
iajs-3245	80	7	used	use	VERB
iajs-3245	80	8	to	to	PART
iajs-3245	80	9	forecast	forecast	VERB
iajs-3245	80	10	the	the	DET
iajs-3245	80	11	response	response	NOUN
iajs-3245	80	12	for	for	ADP
iajs-3245	80	13	specific	specific	ADJ
iajs-3245	80	14	different	different	ADJ
iajs-3245	80	15	values	value	NOUN
iajs-3245	80	16	.	.	PUNCT
iajs-3245	81	1	the	the	DET
iajs-3245	81	2	responses	response	NOUN
iajs-3245	81	3	are	be	AUX
iajs-3245	81	4	plotted	plot	VERB
iajs-3245	81	5	as	as	ADP
iajs-3245	81	6	histograms	histogram	NOUN
iajs-3245	81	7	,	,	PUNCT
iajs-3245	81	8	figure	figure	NOUN
iajs-3245	81	9	1	1	NUM
iajs-3245	81	10	,	,	PUNCT
iajs-3245	81	11	for	for	ADP
iajs-3245	81	12	all	all	DET
iajs-3245	81	13	the	the	DET
iajs-3245	81	14	prediction	prediction	NOUN
iajs-3245	81	15	samples	sample	NOUN
iajs-3245	81	16	,	,	PUNCT
iajs-3245	81	17	and	and	CCONJ
iajs-3245	81	18	the	the	DET
iajs-3245	81	19	exact	exact	ADJ
iajs-3245	81	20	value	value	NOUN
iajs-3245	81	21	are	be	AUX
iajs-3245	81	22	notified	notify	VERB
iajs-3245	81	23	with	with	ADP
iajs-3245	81	24	the	the	DET
iajs-3245	81	25	blue	blue	ADJ
iajs-3245	81	26	lines	line	NOUN
iajs-3245	81	27	.	.	PUNCT
iajs-3245	82	1	figure	figure	VERB
iajs-3245	82	2	1	1	NUM
iajs-3245	82	3	.	.	PUNCT
iajs-3245	82	4	posterior	posterior	ADJ
iajs-3245	82	5	mean	mean	NOUN
iajs-3245	82	6	for	for	ADP
iajs-3245	82	7	the	the	DET
iajs-3245	82	8	parameters	parameter	NOUN
iajs-3245	82	9	.	.	PUNCT
iajs-3245	83	1	table	table	NOUN
iajs-3245	83	2	1	1	NUM
iajs-3245	83	3	.	.	PUNCT
iajs-3245	83	4	compares	compare	VERB
iajs-3245	83	5	the	the	DET
iajs-3245	83	6	exact	exact	ADJ
iajs-3245	83	7	parameter	parameter	NOUN
iajs-3245	83	8	values	value	NOUN
iajs-3245	83	9	with	with	ADP
iajs-3245	83	10	the	the	DET
iajs-3245	83	11	classical	classical	ADJ
iajs-3245	83	12	logistic	logistic	ADJ
iajs-3245	83	13	estimation	estimation	NOUN
iajs-3245	83	14	,	,	PUNCT
iajs-3245	83	15	and	and	CCONJ
iajs-3245	83	16	posterior	posterior	ADJ
iajs-3245	83	17	sample	sample	NOUN
iajs-3245	83	18	mean	mean	VERB
iajs-3245	83	19	.	.	PUNCT
iajs-3245	84	1	also	also	ADV
iajs-3245	84	2	,	,	PUNCT
iajs-3245	84	3	95	95	NUM
iajs-3245	84	4	%	%	NOUN
iajs-3245	84	5	credible	credible	ADJ
iajs-3245	84	6	intervals	interval	NOUN
iajs-3245	84	7	(	(	PUNCT
iajs-3245	84	8	𝐶.	𝐶.	PROPN
iajs-3245	84	9	𝐼.)are	𝐼.)are	NOUN
iajs-3245	84	10	created	create	VERB
iajs-3245	84	11	for	for	ADP
iajs-3245	84	12	all	all	DET
iajs-3245	84	13	the	the	DET
iajs-3245	84	14	parameters	parameter	NOUN
iajs-3245	84	15	.	.	PUNCT
iajs-3245	85	1	the	the	DET
iajs-3245	85	2	exact	exact	ADJ
iajs-3245	85	3	values	value	NOUN
iajs-3245	85	4	lie	lie	VERB
iajs-3245	85	5	inside	inside	ADP
iajs-3245	85	6	the	the	DET
iajs-3245	85	7	95	95	NUM
iajs-3245	85	8	%	%	NOUN
iajs-3245	85	9	𝐶.	𝐶.	PROPN
iajs-3245	85	10	𝐼.	𝐼.	PROPN
iajs-3245	85	11	which	which	PRON
iajs-3245	85	12	implies	imply	VERB
iajs-3245	85	13	that	that	SCONJ
iajs-3245	85	14	the	the	DET
iajs-3245	85	15	approximation	approximation	NOUN
iajs-3245	85	16	is	be	AUX
iajs-3245	85	17	acceptable	acceptable	ADJ
iajs-3245	85	18	[	[	X
iajs-3245	85	19	23	23	NUM
iajs-3245	85	20	]	]	PUNCT
iajs-3245	85	21	.	.	PUNCT
iajs-3245	86	1	table	table	NOUN
iajs-3245	86	2	1	1	NUM
iajs-3245	86	3	.	.	PUNCT
iajs-3245	87	1	estimation	estimation	NOUN
iajs-3245	87	2	of	of	ADP
iajs-3245	87	3	parameters	parameter	NOUN
iajs-3245	87	4	using	use	VERB
iajs-3245	87	5	classical	classical	ADJ
iajs-3245	87	6	logistic	logistic	ADJ
iajs-3245	87	7	and	and	CCONJ
iajs-3245	87	8	posterior	posterior	ADJ
iajs-3245	87	9	distribution	distribution	NOUN
iajs-3245	87	10	.	.	PUNCT
iajs-3245	88	1	true	true	ADJ
iajs-3245	88	2	value	value	NOUN
iajs-3245	88	3	classical	classical	ADJ
iajs-3245	88	4	logistic	logistic	ADJ
iajs-3245	88	5	posterior	posterior	ADJ
iajs-3245	88	6	mean	mean	NOUN
iajs-3245	88	7	𝟗𝟓%	𝟗𝟓%	PROPN
iajs-3245	88	8	𝑪.	𝑪.	PROPN
iajs-3245	88	9	𝑰.	𝑰.	PROPN
iajs-3245	88	10	𝜷𝟎	𝜷𝟎	PROPN
iajs-3245	88	11	2.41	2.41	NUM
iajs-3245	88	12	2.12	2.12	NUM
iajs-3245	88	13	2.45	2.45	NUM
iajs-3245	88	14	(	(	PUNCT
iajs-3245	88	15	2.23	2.23	NUM
iajs-3245	88	16	,	,	PUNCT
iajs-3245	88	17	2.53	2.53	NUM
iajs-3245	88	18	)	)	PUNCT
iajs-3245	88	19	𝜷𝟏	𝜷𝟏	NOUN
iajs-3245	88	20	-1.11	-1.11	NOUN
iajs-3245	88	21	-1.43	-1.43	NUM
iajs-3245	88	22	-1.18	-1.18	X
iajs-3245	88	23	(	(	PUNCT
iajs-3245	88	24	-1.37	-1.37	NOUN
iajs-3245	88	25	,	,	PUNCT
iajs-3245	88	26	-1.04	-1.04	NOUN
iajs-3245	88	27	)	)	PUNCT
iajs-3245	88	28	𝜷𝟐	𝜷𝟐	NOUN
iajs-3245	88	29	4.72	4.72	NUM
iajs-3245	88	30	4.83	4.83	NUM
iajs-3245	88	31	4.72	4.72	NUM
iajs-3245	88	32	(	(	PUNCT
iajs-3245	88	33	4.62	4.62	NUM
iajs-3245	88	34	,	,	PUNCT
iajs-3245	88	35	4.82	4.82	NUM
iajs-3245	88	36	)	)	PUNCT
iajs-3245	88	37	𝜷𝟑	𝜷𝟑	ADV
iajs-3245	88	38	0.31	0.31	NUM
iajs-3245	88	39	0.21	0.21	NUM
iajs-3245	88	40	0.25	0.25	NUM
iajs-3245	88	41	(	(	PUNCT
iajs-3245	88	42	0.12	0.12	NUM
iajs-3245	88	43	,	,	PUNCT
iajs-3245	88	44	0.42	0.42	NUM
iajs-3245	88	45	)	)	PUNCT
iajs-3245	88	46	𝜷𝟒	𝜷𝟒	NOUN
iajs-3245	88	47	4.10	4.10	NUM
iajs-3245	88	48	4.17	4.17	NUM
iajs-3245	88	49	4.12	4.12	NUM
iajs-3245	88	50	(	(	PUNCT
iajs-3245	88	51	4.02	4.02	NUM
iajs-3245	88	52	,	,	PUNCT
iajs-3245	88	53	4.17	4.17	NUM
iajs-3245	88	54	)	)	PUNCT
iajs-3245	88	55	𝝈𝟐	𝝈𝟐	NOUN
iajs-3245	88	56	0.18	0.18	NUM
iajs-3245	88	57	0.16	0.16	NUM
iajs-3245	88	58	0.17	0.17	NUM
iajs-3245	88	59	(	(	PUNCT
iajs-3245	88	60	0.15	0.15	NUM
iajs-3245	88	61	,	,	PUNCT
iajs-3245	88	62	0.19	0.19	NUM
iajs-3245	88	63	)	)	PUNCT
iajs-3245	88	64	b0	b0	VERB
iajs-3245	88	65	2.2	2.2	NUM
iajs-3245	88	66	2.3	2.3	NUM
iajs-3245	88	67	2.4	2.4	NUM
iajs-3245	88	68	2.5	2.5	NUM
iajs-3245	88	69	2.6	2.6	NUM
iajs-3245	88	70	2.7	2.7	NUM
iajs-3245	88	71	b1	b1	NOUN
iajs-3245	88	72	−1.5	−1.5	PROPN
iajs-3245	89	1	−1.4	−1.4	PROPN
iajs-3245	89	2	−1.3	−1.3	PROPN
iajs-3245	89	3	−1.2	−1.2	PROPN
iajs-3245	89	4	−1.1	−1.1	NOUN
iajs-3245	89	5	−1.0	−1.0	PROPN
iajs-3245	89	6	−0.9	−0.9	PROPN
iajs-3245	89	7	b2	b2	NOUN
iajs-3245	89	8	4.4	4.4	NUM
iajs-3245	89	9	4.5	4.5	NUM
iajs-3245	89	10	4.6	4.6	NUM
iajs-3245	89	11	4.7	4.7	NUM
iajs-3245	89	12	4.8	4.8	NUM
iajs-3245	89	13	4.9	4.9	NUM
iajs-3245	89	14	5.0	5.0	NUM
iajs-3245	89	15	b3	b3	NOUN
iajs-3245	89	16	0.0	0.0	NUM
iajs-3245	89	17	0.1	0.1	NUM
iajs-3245	89	18	0.2	0.2	NUM
iajs-3245	89	19	0.3	0.3	NUM
iajs-3245	89	20	0.4	0.4	NUM
iajs-3245	89	21	0.5	0.5	NUM
iajs-3245	89	22	b4	b4	NOUN
iajs-3245	89	23	3.95	3.95	NUM
iajs-3245	89	24	4.00	4.00	NUM
iajs-3245	89	25	4.05	4.05	NUM
iajs-3245	89	26	4.10	4.10	NUM
iajs-3245	89	27	4.15	4.15	NUM
iajs-3245	89	28	4.20	4.20	NUM
iajs-3245	89	29	4.25	4.25	NUM
iajs-3245	89	30	4.30	4.30	NUM
iajs-3245	89	31	s	s	NOUN
iajs-3245	89	32	2	2	NUM
iajs-3245	89	33	0.14	0.14	NUM
iajs-3245	89	34	0.16	0.16	NUM
iajs-3245	89	35	0.18	0.18	NUM
iajs-3245	89	36	0.20	0.20	NUM
iajs-3245	89	37	0.22	0.22	NUM
iajs-3245	89	38	0.24	0.24	NUM
iajs-3245	89	39	ihjpas	ihjpa	NOUN
iajs-3245	89	40	.	.	PUNCT
iajs-3245	90	1	2024	2024	NUM
iajs-3245	90	2	,	,	PUNCT
iajs-3245	90	3	37(4	37(4	PRON
iajs-3245	90	4	)	)	PUNCT
iajs-3245	90	5	396	396	NUM
iajs-3245	90	6	5	5	NUM
iajs-3245	90	7	.	.	PUNCT
iajs-3245	90	8	real	real	ADJ
iajs-3245	90	9	dataset	dataset	NOUN
iajs-3245	90	10	application	application	NOUN
iajs-3245	90	11	the	the	DET
iajs-3245	90	12	proposed	propose	VERB
iajs-3245	90	13	method	method	NOUN
iajs-3245	90	14	has	have	AUX
iajs-3245	90	15	been	be	AUX
iajs-3245	90	16	applied	apply	VERB
iajs-3245	90	17	to	to	ADP
iajs-3245	90	18	the	the	DET
iajs-3245	90	19	real	real	ADJ
iajs-3245	90	20	health	health	NOUN
iajs-3245	90	21	dataset	dataset	NOUN
iajs-3245	90	22	.	.	PUNCT
iajs-3245	91	1	we	we	PRON
iajs-3245	91	2	used	use	VERB
iajs-3245	91	3	the	the	DET
iajs-3245	91	4	heart	heart	NOUN
iajs-3245	91	5	disease	disease	NOUN
iajs-3245	91	6	dataset	dataset	VERB
iajs-3245	91	7	as	as	ADP
iajs-3245	91	8	an	an	DET
iajs-3245	91	9	application	application	NOUN
iajs-3245	91	10	to	to	ADP
iajs-3245	91	11	our	our	PRON
iajs-3245	91	12	work	work	NOUN
iajs-3245	91	13	.	.	PUNCT
iajs-3245	92	1	the	the	DET
iajs-3245	92	2	dataset	dataset	NOUN
iajs-3245	92	3	has	have	AUX
iajs-3245	92	4	been	be	AUX
iajs-3245	92	5	downloaded	download	VERB
iajs-3245	92	6	from	from	ADP
iajs-3245	92	7	the	the	DET
iajs-3245	92	8	uci	uci	PROPN
iajs-3245	92	9	data	data	PROPN
iajs-3245	92	10	science	science	NOUN
iajs-3245	92	11	website	website	NOUN
iajs-3245	92	12	(	(	PUNCT
iajs-3245	92	13	https://www.kaggle.com	https://www.kaggle.com	X
iajs-3245	92	14	)	)	PUNCT
iajs-3245	92	15	.	.	PUNCT
iajs-3245	93	1	it	it	PRON
iajs-3245	93	2	contains	contain	VERB
iajs-3245	93	3	patients	patient	NOUN
iajs-3245	93	4	’	’	PART
iajs-3245	93	5	health	health	NOUN
iajs-3245	93	6	history	history	NOUN
iajs-3245	93	7	and	and	CCONJ
iajs-3245	93	8	many	many	ADJ
iajs-3245	93	9	medical	medical	ADJ
iajs-3245	93	10	indicators	indicator	NOUN
iajs-3245	93	11	for	for	ADP
iajs-3245	93	12	each	each	DET
iajs-3245	93	13	patient	patient	NOUN
iajs-3245	93	14	.	.	PUNCT
iajs-3245	94	1	in	in	ADP
iajs-3245	94	2	the	the	DET
iajs-3245	94	3	following	following	ADJ
iajs-3245	94	4	section	section	NOUN
iajs-3245	94	5	,	,	PUNCT
iajs-3245	94	6	a	a	DET
iajs-3245	94	7	brief	brief	ADJ
iajs-3245	94	8	description	description	NOUN
iajs-3245	94	9	of	of	ADP
iajs-3245	94	10	the	the	DET
iajs-3245	94	11	heart	heart	NOUN
iajs-3245	94	12	disease	disease	NOUN
iajs-3245	94	13	dataset	dataset	NOUN
iajs-3245	94	14	is	be	AUX
iajs-3245	94	15	given	give	VERB
iajs-3245	94	16	.	.	PUNCT
iajs-3245	95	1	heart	heart	NOUN
iajs-3245	95	2	disease	disease	NOUN
iajs-3245	95	3	dataset	dataset	VERB
iajs-3245	95	4	analysis	analysis	NOUN
iajs-3245	95	5	the	the	DET
iajs-3245	95	6	description	description	NOUN
iajs-3245	95	7	of	of	ADP
iajs-3245	95	8	the	the	DET
iajs-3245	95	9	variables	variable	NOUN
iajs-3245	95	10	is	be	AUX
iajs-3245	95	11	given	give	VERB
iajs-3245	95	12	as	as	SCONJ
iajs-3245	95	13	shown	show	VERB
iajs-3245	95	14	in	in	ADP
iajs-3245	95	15	table	table	NOUN
iajs-3245	95	16	2	2	NUM
iajs-3245	95	17	.	.	PUNCT
iajs-3245	96	1	the	the	DET
iajs-3245	96	2	dataset	dataset	NOUN
iajs-3245	96	3	consists	consist	VERB
iajs-3245	96	4	of	of	ADP
iajs-3245	96	5	303	303	NUM
iajs-3245	96	6	observations	observation	NOUN
iajs-3245	96	7	and	and	CCONJ
iajs-3245	96	8	12	12	NUM
iajs-3245	96	9	variables	variable	NOUN
iajs-3245	96	10	:	:	PUNCT
iajs-3245	96	11	age	age	NOUN
iajs-3245	96	12	,	,	PUNCT
iajs-3245	96	13	sex	sex	NOUN
iajs-3245	96	14	,	,	PUNCT
iajs-3245	96	15	cp	cp	NOUN
iajs-3245	96	16	,	,	PUNCT
iajs-3245	96	17	trestbps	trestbps	NOUN
iajs-3245	96	18	,	,	PUNCT
iajs-3245	96	19	chol	chol	PROPN
iajs-3245	96	20	,	,	PUNCT
iajs-3245	96	21	fbs	fbs	PROPN
iajs-3245	96	22	,	,	PUNCT
iajs-3245	96	23	restecg	restecg	NOUN
iajs-3245	96	24	,	,	PUNCT
iajs-3245	96	25	thalach	thalach	ADV
iajs-3245	96	26	,	,	PUNCT
iajs-3245	96	27	exang	exang	PROPN
iajs-3245	96	28	,	,	PUNCT
iajs-3245	96	29	slop	slop	PROPN
iajs-3245	96	30	,	,	PUNCT
iajs-3245	96	31	ca	ca	NOUN
iajs-3245	96	32	,	,	PUNCT
iajs-3245	96	33	and	and	CCONJ
iajs-3245	96	34	thal	thal	NOUN
iajs-3245	96	35	.	.	PUNCT
iajs-3245	97	1	the	the	DET
iajs-3245	97	2	aim	aim	NOUN
iajs-3245	97	3	is	be	AUX
iajs-3245	97	4	to	to	PART
iajs-3245	97	5	forecast	forecast	VERB
iajs-3245	97	6	whether	whether	SCONJ
iajs-3245	97	7	an	an	DET
iajs-3245	97	8	individual	individual	NOUN
iajs-3245	97	9	has	have	VERB
iajs-3245	97	10	heart	heart	NOUN
iajs-3245	97	11	disease	disease	NOUN
iajs-3245	97	12	depending	depend	VERB
iajs-3245	97	13	on	on	ADP
iajs-3245	97	14	the	the	DET
iajs-3245	97	15	features	feature	NOUN
iajs-3245	97	16	or	or	CCONJ
iajs-3245	97	17	not	not	PART
iajs-3245	97	18	[	[	X
iajs-3245	97	19	24	24	NUM
iajs-3245	97	20	]	]	PUNCT
iajs-3245	97	21	.	.	PUNCT
iajs-3245	98	1	table	table	NOUN
iajs-3245	98	2	2	2	NUM
iajs-3245	98	3	.	.	PUNCT
iajs-3245	98	4	descriptive	descriptive	ADJ
iajs-3245	98	5	table	table	NOUN
iajs-3245	98	6	for	for	ADP
iajs-3245	98	7	the	the	DET
iajs-3245	98	8	variables	variable	NOUN
iajs-3245	98	9	no	no	INTJ
iajs-3245	98	10	.	.	PUNCT
iajs-3245	99	1	predicters	predicter	NOUN
iajs-3245	99	2	descriptive	descriptive	VERB
iajs-3245	99	3	1	1	NUM
iajs-3245	99	4	age	age	NOUN
iajs-3245	99	5	the	the	DET
iajs-3245	99	6	age	age	NOUN
iajs-3245	99	7	in	in	ADP
iajs-3245	99	8	years	year	NOUN
iajs-3245	99	9	for	for	ADP
iajs-3245	99	10	patients	patient	NOUN
iajs-3245	99	11	2	2	NUM
iajs-3245	99	12	sex	sex	NOUN
iajs-3245	99	13	male	male	NOUN
iajs-3245	99	14	=	=	NOUN
iajs-3245	99	15	1	1	NUM
iajs-3245	99	16	;	;	PUNCT
iajs-3245	99	17	female	female	ADJ
iajs-3245	99	18	=	=	SYM
iajs-3245	99	19	0	0	NUM
iajs-3245	99	20	3	3	NUM
iajs-3245	99	21	cp	cp	INTJ
iajs-3245	99	22	the	the	DET
iajs-3245	99	23	chest	chest	NOUN
iajs-3245	99	24	pressure	pressure	NOUN
iajs-3245	99	25	that	that	PRON
iajs-3245	99	26	was	be	AUX
iajs-3245	99	27	felt	feel	VERB
iajs-3245	99	28	:	:	PUNCT
iajs-3245	99	29	value	value	NOUN
iajs-3245	99	30	1	1	NUM
iajs-3245	99	31	denotes	denote	NOUN
iajs-3245	99	32	normal	normal	ADJ
iajs-3245	99	33	angina	angina	NOUN
iajs-3245	99	34	,	,	PUNCT
iajs-3245	99	35	value	value	NOUN
iajs-3245	99	36	2	2	NUM
iajs-3245	99	37	denotes	denote	NOUN
iajs-3245	99	38	atypical	atypical	ADJ
iajs-3245	99	39	angina	angina	NOUN
iajs-3245	99	40	,	,	PUNCT
iajs-3245	99	41	value	value	VERB
iajs-3245	99	42	3	3	NUM
iajs-3245	99	43	denotes	denote	NOUN
iajs-3245	99	44	non	non	ADJ
iajs-3245	99	45	-	-	ADJ
iajs-3245	99	46	anginal	anginal	ADJ
iajs-3245	99	47	discomfort	discomfort	NOUN
iajs-3245	99	48	,	,	PUNCT
iajs-3245	99	49	and	and	CCONJ
iajs-3245	99	50	value	value	VERB
iajs-3245	99	51	4	4	NUM
iajs-3245	99	52	denotes	denote	NOUN
iajs-3245	99	53	asymptomatic	asymptomatic	ADJ
iajs-3245	99	54	4	4	NUM
iajs-3245	99	55	trestbps	trestbps	NOUN
iajs-3245	99	56	on	on	ADP
iajs-3245	99	57	admission	admission	NOUN
iajs-3245	99	58	to	to	ADP
iajs-3245	99	59	the	the	DET
iajs-3245	99	60	facility	facility	NOUN
iajs-3245	99	61	,	,	PUNCT
iajs-3245	99	62	the	the	DET
iajs-3245	99	63	patient	patient	NOUN
iajs-3245	99	64	's	's	PART
iajs-3245	99	65	resting	rest	VERB
iajs-3245	99	66	blood	blood	NOUN
iajs-3245	99	67	pressure	pressure	NOUN
iajs-3245	99	68	was	be	AUX
iajs-3245	99	69	measured	measure	VERB
iajs-3245	99	70	in	in	ADP
iajs-3245	99	71	millimeters	millimeter	NOUN
iajs-3245	99	72	of	of	ADP
iajs-3245	99	73	mercury	mercury	NOUN
iajs-3245	99	74	(	(	PUNCT
iajs-3245	99	75	mm	mm	PROPN
iajs-3245	99	76	hg	hg	NOUN
iajs-3245	99	77	)	)	PUNCT
iajs-3245	99	78	.	.	PUNCT
iajs-3245	100	1	5	5	NUM
iajs-3245	100	2	chol	chol	NOUN
iajs-3245	100	3	cholesterol	cholesterol	NOUN
iajs-3245	100	4	levels	level	NOUN
iajs-3245	100	5	in	in	ADP
iajs-3245	100	6	milligrams	milligram	NOUN
iajs-3245	100	7	per	per	ADP
iajs-3245	100	8	deciliter	deciliter	NOUN
iajs-3245	100	9	6	6	NUM
iajs-3245	100	10	fbs	fbs	ADJ
iajs-3245	100	11	fasting	fast	VERB
iajs-3245	100	12	blood	blood	NOUN
iajs-3245	100	13	sugar	sugar	NOUN
iajs-3245	100	14	is	be	AUX
iajs-3245	100	15	over	over	ADP
iajs-3245	100	16	120	120	NUM
iajs-3245	100	17	mg	mg	PROPN
iajs-3245	100	18	/	/	SYM
iajs-3245	100	19	dl	dl	NOUN
iajs-3245	100	20	:	:	PUNCT
iajs-3245	100	21	true	true	ADJ
iajs-3245	100	22	=	=	ADJ
iajs-3245	100	23	1	1	NUM
iajs-3245	100	24	;	;	PUNCT
iajs-3245	100	25	false	false	ADJ
iajs-3245	100	26	=	=	SYM
iajs-3245	100	27	0	0	NUM
iajs-3245	100	28	7	7	NUM
iajs-3245	100	29	restecg	restecg	NOUN
iajs-3245	100	30	resting	rest	VERB
iajs-3245	100	31	electrocardiographic	electrocardiographic	ADJ
iajs-3245	100	32	measurement	measurement	NOUN
iajs-3245	100	33	:	:	PUNCT
iajs-3245	100	34	regular	regular	ADJ
iajs-3245	100	35	=	=	SYM
iajs-3245	100	36	0	0	NUM
iajs-3245	100	37	,	,	PUNCT
iajs-3245	100	38	st	st	PROPN
iajs-3245	100	39	-	-	PUNCT
iajs-3245	100	40	t	t	PROPN
iajs-3245	100	41	wave	wave	NOUN
iajs-3245	100	42	abnormality	abnormality	NOUN
iajs-3245	100	43	=	=	SYM
iajs-3245	100	44	1	1	NUM
iajs-3245	100	45	,	,	PUNCT
iajs-3245	100	46	potential	potential	ADJ
iajs-3245	100	47	or	or	CCONJ
iajs-3245	100	48	definite	definite	ADJ
iajs-3245	100	49	left	left	ADJ
iajs-3245	100	50	ventricular	ventricular	ADJ
iajs-3245	100	51	hypertrophy	hypertrophy	NOUN
iajs-3245	100	52	according	accord	VERB
iajs-3245	100	53	to	to	ADP
iajs-3245	100	54	estes	este	NOUN
iajs-3245	100	55	'	'	PART
iajs-3245	100	56	criteria	criterion	NOUN
iajs-3245	100	57	=	=	PUNCT
iajs-3245	100	58	3	3	X
iajs-3245	100	59	.	.	NOUN
iajs-3245	100	60	8	8	NUM
iajs-3245	100	61	thalach	thalach	NOUN
iajs-3245	100	62	attained	attain	VERB
iajs-3245	100	63	optimum	optimum	ADJ
iajs-3245	100	64	heart	heart	NOUN
iajs-3245	100	65	rate	rate	NOUN
iajs-3245	100	66	9	9	NUM
iajs-3245	100	67	exang	exang	NOUN
iajs-3245	100	68	angina	angina	NOUN
iajs-3245	100	69	caused	cause	VERB
iajs-3245	100	70	by	by	ADP
iajs-3245	100	71	workout	workout	NOUN
iajs-3245	100	72	:	:	PUNCT
iajs-3245	101	1	yes	yes	INTJ
iajs-3245	101	2	=	=	SYM
iajs-3245	101	3	1	1	NUM
iajs-3245	101	4	;	;	PUNCT
iajs-3245	101	5	no	no	DET
iajs-3245	101	6	=	=	NOUN
iajs-3245	101	7	0	0	NUM
iajs-3245	101	8	10	10	NUM
iajs-3245	101	9	slop	slop	NOUN
iajs-3245	101	10	the	the	DET
iajs-3245	101	11	slope	slope	NOUN
iajs-3245	101	12	of	of	ADP
iajs-3245	101	13	the	the	DET
iajs-3245	101	14	most	most	ADV
iajs-3245	101	15	difficult	difficult	ADJ
iajs-3245	101	16	workout	workout	NOUN
iajs-3245	101	17	segment	segment	NOUN
iajs-3245	101	18	:	:	PUNCT
iajs-3245	101	19	value	value	NOUN
iajs-3245	101	20	1	1	NUM
iajs-3245	101	21	indicates	indicate	VERB
iajs-3245	101	22	an	an	DET
iajs-3245	101	23	upslope	upslope	NOUN
iajs-3245	101	24	,	,	PUNCT
iajs-3245	101	25	value	value	NOUN
iajs-3245	101	26	2	2	NUM
iajs-3245	101	27	indicates	indicate	VERB
iajs-3245	101	28	a	a	DET
iajs-3245	101	29	smooth	smooth	ADJ
iajs-3245	101	30	surface	surface	NOUN
iajs-3245	101	31	,	,	PUNCT
iajs-3245	101	32	and	and	CCONJ
iajs-3245	101	33	value	value	NOUN
iajs-3245	101	34	3	3	NUM
iajs-3245	101	35	indicates	indicate	VERB
iajs-3245	101	36	a	a	DET
iajs-3245	101	37	downslope	downslope	NOUN
iajs-3245	101	38	.	.	PUNCT
iajs-3245	102	1	11	11	NUM
iajs-3245	102	2	ca	can	AUX
iajs-3245	102	3	total	total	NOUN
iajs-3245	102	4	of	of	ADP
iajs-3245	102	5	major	major	ADJ
iajs-3245	102	6	vessels	vessel	NOUN
iajs-3245	102	7	colored	color	VERB
iajs-3245	102	8	by	by	ADP
iajs-3245	102	9	flourosopy	flourosopy	NOUN
iajs-3245	102	10	(	(	PUNCT
iajs-3245	102	11	0	0	NUM
iajs-3245	102	12	3	3	NUM
iajs-3245	102	13	)	)	PUNCT
iajs-3245	102	14	12	12	NUM
iajs-3245	102	15	thal	thal	NOUN
iajs-3245	102	16	thalassemia	thalassemia	NOUN
iajs-3245	102	17	is	be	AUX
iajs-3245	102	18	a	a	DET
iajs-3245	102	19	form	form	NOUN
iajs-3245	102	20	of	of	ADP
iajs-3245	102	21	blood	blood	NOUN
iajs-3245	102	22	disorder	disorder	NOUN
iajs-3245	102	23	:	:	PUNCT
iajs-3245	102	24	standard=3	standard=3	PROPN
iajs-3245	102	25	,	,	PUNCT
iajs-3245	102	26	fixed=6	fixed=6	PROPN
iajs-3245	102	27	,	,	PUNCT
iajs-3245	102	28	and	and	CCONJ
iajs-3245	102	29	reversible=7	reversible=7	PROPN
iajs-3245	102	30	.	.	PUNCT
iajs-3245	103	1	the	the	DET
iajs-3245	103	2	correlation	correlation	NOUN
iajs-3245	103	3	matrix	matrix	NOUN
iajs-3245	103	4	for	for	ADP
iajs-3245	103	5	predictors	predictor	NOUN
iajs-3245	103	6	in	in	ADP
iajs-3245	103	7	the	the	DET
iajs-3245	103	8	heart	heart	NOUN
iajs-3245	103	9	disease	disease	NOUN
iajs-3245	103	10	dataset	dataset	NOUN
iajs-3245	103	11	are	be	AUX
iajs-3245	103	12	shown	show	VERB
iajs-3245	103	13	in	in	ADP
iajs-3245	103	14	figure	figure	NOUN
iajs-3245	103	15	2	2	NUM
iajs-3245	103	16	.	.	PUNCT
iajs-3245	104	1	all	all	DET
iajs-3245	104	2	the	the	DET
iajs-3245	104	3	predictors	predictor	NOUN
iajs-3245	104	4	do	do	AUX
iajs-3245	104	5	not	not	PART
iajs-3245	104	6	have	have	VERB
iajs-3245	104	7	significant	significant	ADJ
iajs-3245	104	8	correlations	correlation	NOUN
iajs-3245	104	9	.	.	PUNCT
iajs-3245	105	1	however	however	ADV
iajs-3245	105	2	,	,	PUNCT
iajs-3245	105	3	the	the	DET
iajs-3245	105	4	𝑠𝑙𝑜𝑝𝑒	𝑠𝑙𝑜𝑝𝑒	NOUN
iajs-3245	105	5	has	have	VERB
iajs-3245	105	6	a	a	DET
iajs-3245	105	7	positive	positive	ADJ
iajs-3245	105	8	correlation	correlation	NOUN
iajs-3245	105	9	with	with	ADP
iajs-3245	105	10	𝑡ℎ𝑎𝑙𝑎𝑐ℎ	𝑡ℎ𝑎𝑙𝑎𝑐ℎ	NOUN
iajs-3245	105	11	,	,	PUNCT
iajs-3245	105	12	𝑎𝑔𝑒	𝑎𝑔𝑒	NOUN
iajs-3245	105	13	has	have	VERB
iajs-3245	105	14	negative	negative	ADJ
iajs-3245	105	15	correlation	correlation	NOUN
iajs-3245	105	16	with	with	ADP
iajs-3245	105	17	𝑡ℎ𝑎𝑙𝑎𝑐ℎ	𝑡ℎ𝑎𝑙𝑎𝑐ℎ	NOUN
iajs-3245	105	18	,	,	PUNCT
iajs-3245	105	19	and	and	CCONJ
iajs-3245	105	20	𝑡ℎ𝑎𝑙𝑎𝑐ℎ	𝑡ℎ𝑎𝑙𝑎𝑐ℎ	NOUN
iajs-3245	105	21	has	have	VERB
iajs-3245	105	22	positive	positive	ADJ
iajs-3245	105	23	and	and	CCONJ
iajs-3245	105	24	negative	negative	ADJ
iajs-3245	105	25	correlation	correlation	NOUN
iajs-3245	105	26	with	with	ADP
iajs-3245	105	27	both	both	DET
iajs-3245	105	28	𝑠𝑙𝑜𝑝	𝑠𝑙𝑜𝑝	NOUN
iajs-3245	105	29	and	and	CCONJ
iajs-3245	105	30	𝑒𝑥𝑎𝑛𝑔	𝑒𝑥𝑎𝑛𝑔	NOUN
iajs-3245	105	31	;	;	PUNCT
iajs-3245	105	32	respectively	respectively	ADV
iajs-3245	105	33	.	.	PUNCT
iajs-3245	106	1	the	the	DET
iajs-3245	106	2	data	datum	NOUN
iajs-3245	106	3	set	set	VERB
iajs-3245	106	4	is	be	AUX
iajs-3245	106	5	visualized	visualize	VERB
iajs-3245	106	6	in	in	ADP
iajs-3245	106	7	figure	figure	NOUN
iajs-3245	106	8	3	3	NUM
iajs-3245	106	9	.	.	PUNCT
iajs-3245	106	10	where	where	SCONJ
iajs-3245	106	11	it	it	PRON
iajs-3245	106	12	is	be	AUX
iajs-3245	106	13	plotted	plot	VERB
iajs-3245	106	14	as	as	ADP
iajs-3245	106	15	a	a	DET
iajs-3245	106	16	scatter	scatter	NOUN
iajs-3245	106	17	plot	plot	NOUN
iajs-3245	106	18	.	.	PUNCT
iajs-3245	107	1	the	the	DET
iajs-3245	107	2	x	x	NOUN
iajs-3245	107	3	-	-	NOUN
iajs-3245	107	4	axis	axis	ADJ
iajs-3245	107	5	and	and	CCONJ
iajs-3245	107	6	y	y	NOUN
iajs-3245	107	7	-	-	PUNCT
iajs-3245	107	8	axis	axis	NOUN
iajs-3245	107	9	are	be	AUX
iajs-3245	107	10	chosen	choose	VERB
iajs-3245	107	11	to	to	PART
iajs-3245	107	12	be	be	AUX
iajs-3245	107	13	chol	chol	NOUN
iajs-3245	107	14	and	and	CCONJ
iajs-3245	107	15	trestbps	trestbps	NOUN
iajs-3245	107	16	,	,	PUNCT
iajs-3245	107	17	respectively	respectively	ADV
iajs-3245	107	18	.	.	PUNCT
iajs-3245	108	1	the	the	DET
iajs-3245	108	2	chest	chest	NOUN
iajs-3245	108	3	pain	pain	NOUN
iajs-3245	108	4	experienced	experienced	ADJ
iajs-3245	108	5	(	(	PUNCT
iajs-3245	108	6	cp	cp	NOUN
iajs-3245	108	7	)	)	PUNCT
iajs-3245	108	8	is	be	AUX
iajs-3245	108	9	used	use	VERB
iajs-3245	108	10	to	to	PART
iajs-3245	108	11	note	note	VERB
iajs-3245	108	12	the	the	DET
iajs-3245	108	13	observations	observation	NOUN
iajs-3245	108	14	for	for	ADP
iajs-3245	108	15	all	all	DET
iajs-3245	108	16	the	the	DET
iajs-3245	108	17	individuals	individual	NOUN
iajs-3245	108	18	.	.	PUNCT
iajs-3245	109	1	https://medium.com/@halima23121998/heart-disease-uci-logistic-regression-in-r-b95b821088e6?sk=def7a489c8ce9b249048e903d80f8591	https://medium.com/@halima23121998/heart-disease-uci-logistic-regression-in-r-b95b821088e6?sk=def7a489c8ce9b249048e903d80f8591	PROPN
iajs-3245	109	2	ihjpas	ihjpas	PROPN
iajs-3245	109	3	.	.	PUNCT
iajs-3245	110	1	2024	2024	NUM
iajs-3245	110	2	,	,	PUNCT
iajs-3245	110	3	37(4	37(4	PRON
iajs-3245	110	4	)	)	PUNCT
iajs-3245	110	5	397	397	NUM
iajs-3245	110	6	figure	figure	NOUN
iajs-3245	110	7	2	2	NUM
iajs-3245	110	8	.	.	PUNCT
iajs-3245	110	9	correlation	correlation	NOUN
iajs-3245	110	10	matrix	matrix	NOUN
iajs-3245	110	11	for	for	ADP
iajs-3245	110	12	predictors	predictor	NOUN
iajs-3245	110	13	in	in	ADP
iajs-3245	110	14	heart	heart	NOUN
iajs-3245	110	15	disease	disease	NOUN
iajs-3245	110	16	dataset	dataset	VERB
iajs-3245	110	17	.	.	PUNCT
iajs-3245	111	1	figure	figure	NOUN
iajs-3245	111	2	3	3	NUM
iajs-3245	111	3	.	.	PUNCT
iajs-3245	111	4	scatter	scatter	NOUN
iajs-3245	111	5	plot	plot	NOUN
iajs-3245	111	6	(	(	PUNCT
iajs-3245	111	7	trestbps	trestbps	NOUN
iajs-3245	111	8	vs.	vs.	X
iajs-3245	111	9	chol	chol	PROPN
iajs-3245	111	10	)	)	PUNCT
iajs-3245	111	11	.	.	PUNCT
iajs-3245	112	1	6	6	X
iajs-3245	112	2	.	.	X
iajs-3245	112	3	results	result	NOUN
iajs-3245	112	4	and	and	CCONJ
iajs-3245	112	5	discussion	discussion	NOUN
iajs-3245	112	6	the	the	DET
iajs-3245	112	7	dataset	dataset	NOUN
iajs-3245	112	8	was	be	AUX
iajs-3245	112	9	split	split	VERB
iajs-3245	112	10	up	up	ADP
iajs-3245	112	11	into	into	ADP
iajs-3245	112	12	training	training	NOUN
iajs-3245	112	13	(	(	PUNCT
iajs-3245	112	14	212	212	NUM
iajs-3245	112	15	observations	observation	NOUN
iajs-3245	112	16	)	)	PUNCT
iajs-3245	112	17	and	and	CCONJ
iajs-3245	112	18	testing	testing	NOUN
iajs-3245	112	19	(	(	PUNCT
iajs-3245	112	20	91	91	NUM
iajs-3245	112	21	observations	observation	NOUN
iajs-3245	112	22	)	)	PUNCT
iajs-3245	112	23	parts	part	NOUN
iajs-3245	112	24	.	.	PUNCT
iajs-3245	113	1	svm	svm	PROPN
iajs-3245	113	2	,	,	PUNCT
iajs-3245	113	3	ann	ann	PROPN
iajs-3245	113	4	,	,	PUNCT
iajs-3245	113	5	classical	classical	ADJ
iajs-3245	113	6	logistic	logistic	NOUN
iajs-3245	113	7	,	,	PUNCT
iajs-3245	113	8	and	and	CCONJ
iajs-3245	113	9	bayesian	bayesian	NOUN
iajs-3245	113	10	logistic	logistic	ADJ
iajs-3245	113	11	classification	classification	NOUN
iajs-3245	113	12	methods	method	NOUN
iajs-3245	113	13	are	be	AUX
iajs-3245	113	14	applied	apply	VERB
iajs-3245	113	15	for	for	ADP
iajs-3245	113	16	testing	testing	NOUN
iajs-3245	113	17	and	and	CCONJ
iajs-3245	113	18	training	training	NOUN
iajs-3245	113	19	datasets	dataset	NOUN
iajs-3245	113	20	.	.	PUNCT
iajs-3245	114	1	the	the	DET
iajs-3245	114	2	methods	method	NOUN
iajs-3245	114	3	were	be	AUX
iajs-3245	114	4	compared	compare	VERB
iajs-3245	114	5	according	accord	VERB
iajs-3245	114	6	to	to	ADP
iajs-3245	114	7	their	their	PRON
iajs-3245	114	8	accuracy	accuracy	NOUN
iajs-3245	114	9	and	and	CCONJ
iajs-3245	114	10	consuming	consuming	NOUN
iajs-3245	114	11	time	time	NOUN
iajs-3245	114	12	.	.	PUNCT
iajs-3245	115	1	table	table	NOUN
iajs-3245	115	2	3	3	NUM
iajs-3245	115	3	.	.	PUNCT
iajs-3245	115	4	compares	compare	VERB
iajs-3245	115	5	four	four	NUM
iajs-3245	115	6	methods	method	NOUN
iajs-3245	115	7	by	by	ADP
iajs-3245	115	8	calculating	calculate	VERB
iajs-3245	115	9	the	the	DET
iajs-3245	115	10	accuracy	accuracy	NOUN
iajs-3245	115	11	for	for	ADP
iajs-3245	115	12	both	both	PRON
iajs-3245	115	13	training	training	NOUN
iajs-3245	115	14	and	and	CCONJ
iajs-3245	115	15	testing	testing	NOUN
iajs-3245	115	16	datasets	dataset	NOUN
iajs-3245	115	17	and	and	CCONJ
iajs-3245	115	18	the	the	DET
iajs-3245	115	19	total	total	ADJ
iajs-3245	115	20	time	time	NOUN
iajs-3245	115	21	-	-	PUNCT
iajs-3245	115	22	consuming	consume	VERB
iajs-3245	115	23	.	.	PUNCT
iajs-3245	116	1	the	the	DET
iajs-3245	116	2	best	good	ADJ
iajs-3245	116	3	accuracy	accuracy	NOUN
iajs-3245	116	4	exists	exist	VERB
iajs-3245	116	5	in	in	ADP
iajs-3245	116	6	the	the	DET
iajs-3245	116	7	bayesian	bayesian	NOUN
iajs-3245	116	8	logistic	logistic	NOUN
iajs-3245	116	9	method	method	NOUN
iajs-3245	116	10	,	,	PUNCT
iajs-3245	116	11	which	which	PRON
iajs-3245	116	12	is	be	AUX
iajs-3245	116	13	91.38	91.38	NUM
iajs-3245	116	14	%	%	NOUN
iajs-3245	116	15	in	in	ADP
iajs-3245	116	16	the	the	DET
iajs-3245	116	17	training	training	NOUN
iajs-3245	116	18	dataset	dataset	NOUN
iajs-3245	116	19	and	and	CCONJ
iajs-3245	116	20	90.92	90.92	NUM
iajs-3245	116	21	%	%	NOUN
iajs-3245	116	22	in	in	ADP
iajs-3245	116	23	the	the	DET
iajs-3245	116	24	testing	testing	NOUN
iajs-3245	116	25	dataset	dataset	NOUN
iajs-3245	116	26	.	.	PUNCT
iajs-3245	117	1	this	this	PRON
iajs-3245	117	2	indicates	indicate	VERB
iajs-3245	117	3	the	the	DET
iajs-3245	117	4	bayesian	bayesian	NOUN
iajs-3245	117	5	logistic	logistic	ADJ
iajs-3245	117	6	method	method	NOUN
iajs-3245	117	7	is	be	AUX
iajs-3245	117	8	the	the	DET
iajs-3245	117	9	best	good	ADJ
iajs-3245	117	10	.	.	PUNCT
iajs-3245	118	1	also	also	ADV
iajs-3245	118	2	,	,	PUNCT
iajs-3245	118	3	bayesian	bayesian	NOUN
iajs-3245	118	4	logistic	logistic	ADJ
iajs-3245	118	5	consumes	consume	NOUN
iajs-3245	118	6	less	less	ADJ
iajs-3245	118	7	time	time	NOUN
iajs-3245	118	8	than	than	ADP
iajs-3245	118	9	other	other	ADJ
iajs-3245	118	10	methods	method	NOUN
iajs-3245	118	11	.	.	PUNCT
iajs-3245	119	1	−1	−1	NOUN
iajs-3245	119	2	−0.8	−0.8	PROPN
iajs-3245	119	3	−0.6	−0.6	PROPN
iajs-3245	119	4	−0.4	−0.4	PUNCT
iajs-3245	120	1	−0.2	−0.2	PROPN
iajs-3245	120	2	0	0	NUM
iajs-3245	120	3	0.2	0.2	NUM
iajs-3245	120	4	0.4	0.4	NUM
iajs-3245	120	5	0.6	0.6	NUM
iajs-3245	120	6	0.8	0.8	NUM
iajs-3245	120	7	1	1	NUM
iajs-3245	120	8	re	re	NOUN
iajs-3245	120	9	s	s	X
iajs-3245	120	10	te	te	ADP
iajs-3245	120	11	c	c	NOUN
iajs-3245	120	12	g	g	PROPN
iajs-3245	120	13	c	c	PROPN
iajs-3245	120	14	p	p	X
iajs-3245	120	15	th	th	X
iajs-3245	121	1	a	a	X
iajs-3245	121	2	la	la	NOUN
iajs-3245	121	3	c	c	NOUN
iajs-3245	121	4	h	h	NOUN
iajs-3245	121	5	s	s	VERB
iajs-3245	121	6	lo	lo	NOUN
iajs-3245	121	7	p	p	NOUN
iajs-3245	121	8	e	e	X
iajs-3245	121	9	e	e	X
iajs-3245	121	10	x	x	X
iajs-3245	121	11	a	a	DET
iajs-3245	121	12	n	n	NOUN
iajs-3245	121	13	g	g	NOUN
iajs-3245	121	14	s	s	X
iajs-3245	121	15	e	e	X
iajs-3245	121	16	x	x	X
iajs-3245	121	17	th	th	X
iajs-3245	122	1	a	a	DET
iajs-3245	122	2	l	l	NOUN
iajs-3245	122	3	fb	fb	NOUN
iajs-3245	122	4	s	s	PROPN
iajs-3245	122	5	c	c	NOUN
iajs-3245	122	6	a	a	DET
iajs-3245	122	7	c	c	NOUN
iajs-3245	122	8	h	h	NOUN
iajs-3245	122	9	o	o	NOUN
iajs-3245	122	10	l	l	NOUN
iajs-3245	122	11	a	a	DET
iajs-3245	122	12	g	g	NOUN
iajs-3245	122	13	e	e	NOUN
iajs-3245	122	14	tr	tr	NOUN
iajs-3245	122	15	e	e	PROPN
iajs-3245	122	16	s	s	NOUN
iajs-3245	122	17	tb	tb	ADP
iajs-3245	122	18	p	p	NOUN
iajs-3245	122	19	s	s	PROPN
iajs-3245	122	20	restecg	restecg	NOUN
iajs-3245	122	21	cp	cp	INTJ
iajs-3245	122	22	thalach	thalach	NOUN
iajs-3245	122	23	slope	slope	PROPN
iajs-3245	122	24	exang	exang	PROPN
iajs-3245	122	25	sex	sex	PROPN
iajs-3245	122	26	thal	thal	PROPN
iajs-3245	122	27	fbs	fbs	PROPN
iajs-3245	122	28	ca	can	AUX
iajs-3245	122	29	chol	chol	PROPN
iajs-3245	122	30	age	age	NOUN
iajs-3245	122	31	trestbps	trestbps	NOUN
iajs-3245	122	32	80	80	NUM
iajs-3245	122	33	120	120	NUM
iajs-3245	122	34	160	160	NUM
iajs-3245	122	35	200	200	NUM
iajs-3245	122	36	100	100	NUM
iajs-3245	122	37	200	200	NUM
iajs-3245	122	38	300	300	NUM
iajs-3245	122	39	400	400	NUM
iajs-3245	122	40	chol	chol	NOUN
iajs-3245	122	41	tr	tr	VERB
iajs-3245	122	42	e	e	PROPN
iajs-3245	122	43	st	st	PROPN
iajs-3245	122	44	bp	bp	PROPN
iajs-3245	122	45	s	s	PROPN
iajs-3245	122	46	0.00	0.00	NUM
iajs-3245	122	47	0.25	0.25	NUM
iajs-3245	122	48	0.50	0.50	NUM
iajs-3245	122	49	0.75	0.75	NUM
iajs-3245	122	50	1.00	1.00	NUM
iajs-3245	122	51	target	target	NOUN
iajs-3245	122	52	cp	cp	INTJ
iajs-3245	122	53	0	0	NUM
iajs-3245	122	54	1	1	NUM
iajs-3245	122	55	2	2	NUM
iajs-3245	122	56	3	3	NUM
iajs-3245	122	57	scatterplot	scatterplot	NOUN
iajs-3245	122	58	(	(	PUNCT
iajs-3245	122	59	trestbps	trestbps	NOUN
iajs-3245	122	60	vs.	vs.	X
iajs-3245	122	61	chol	chol	PROPN
iajs-3245	122	62	)	)	PUNCT
iajs-3245	122	63	ihjpas	ihjpas	PROPN
iajs-3245	122	64	.	.	PUNCT
iajs-3245	123	1	2024	2024	NUM
iajs-3245	123	2	,	,	PUNCT
iajs-3245	123	3	37(4	37(4	PRON
iajs-3245	123	4	)	)	PUNCT
iajs-3245	123	5	398	398	NUM
iajs-3245	123	6	table	table	NOUN
iajs-3245	123	7	3	3	NUM
iajs-3245	123	8	.	.	NOUN
iajs-3245	123	9	comparison	comparison	NOUN
iajs-3245	123	10	among	among	ADP
iajs-3245	123	11	svm	svm	PROPN
iajs-3245	123	12	,	,	PUNCT
iajs-3245	123	13	ann	ann	PROPN
iajs-3245	123	14	,	,	PUNCT
iajs-3245	123	15	classical	classical	ADJ
iajs-3245	123	16	logistic	logistic	NOUN
iajs-3245	123	17	,	,	PUNCT
iajs-3245	123	18	and	and	CCONJ
iajs-3245	123	19	bayesian	bayesian	NOUN
iajs-3245	123	20	logistic	logistic	ADJ
iajs-3245	123	21	methods	method	NOUN
iajs-3245	123	22	.	.	PUNCT
iajs-3245	124	1	methods	method	NOUN
iajs-3245	124	2	accuracy	accuracy	NOUN
iajs-3245	124	3	total	total	ADJ
iajs-3245	124	4	time	time	NOUN
iajs-3245	124	5	training	train	VERB
iajs-3245	124	6	dataset	dataset	NOUN
iajs-3245	124	7	testing	testing	NOUN
iajs-3245	124	8	dataset	dataset	VERB
iajs-3245	124	9	svm	svm	NOUN
iajs-3245	124	10	88.60	88.60	NUM
iajs-3245	124	11	%	%	NOUN
iajs-3245	124	12	84.42	84.42	NUM
iajs-3245	124	13	%	%	NOUN
iajs-3245	124	14	7	7	NUM
iajs-3245	124	15	seconds	second	NOUN
iajs-3245	124	16	ann	ann	PROPN
iajs-3245	124	17	86.80	86.80	NUM
iajs-3245	124	18	%	%	NOUN
iajs-3245	124	19	80.55	80.55	NUM
iajs-3245	124	20	%	%	NOUN
iajs-3245	124	21	9	9	NUM
iajs-3245	124	22	seconds	second	NOUN
iajs-3245	124	23	classical	classical	ADJ
iajs-3245	124	24	logistic	logistic	NOUN
iajs-3245	124	25	90.10	90.10	NUM
iajs-3245	124	26	%	%	NOUN
iajs-3245	124	27	86.39	86.39	NUM
iajs-3245	124	28	%	%	NOUN
iajs-3245	124	29	6	6	NUM
iajs-3245	124	30	seconds	second	NOUN
iajs-3245	124	31	bayesian	bayesian	NOUN
iajs-3245	124	32	logistic	logistic	VERB
iajs-3245	124	33	91.38	91.38	NUM
iajs-3245	124	34	%	%	NOUN
iajs-3245	124	35	90.92	90.92	NUM
iajs-3245	124	36	%	%	NOUN
iajs-3245	124	37	4	4	NUM
iajs-3245	124	38	seconds	second	NOUN
iajs-3245	124	39	the	the	DET
iajs-3245	124	40	accuracy	accuracy	NOUN
iajs-3245	124	41	curve	curve	NOUN
iajs-3245	124	42	for	for	ADP
iajs-3245	124	43	both	both	CCONJ
iajs-3245	124	44	classical	classical	ADJ
iajs-3245	124	45	and	and	CCONJ
iajs-3245	124	46	bayesian	bayesian	ADJ
iajs-3245	124	47	logistic	logistic	NOUN
iajs-3245	124	48	is	be	AUX
iajs-3245	124	49	shown	show	VERB
iajs-3245	124	50	in	in	ADP
iajs-3245	124	51	figure	figure	NOUN
iajs-3245	124	52	4	4	NUM
iajs-3245	124	53	.	.	PUNCT
iajs-3245	125	1	we	we	PRON
iajs-3245	125	2	can	can	AUX
iajs-3245	125	3	see	see	VERB
iajs-3245	125	4	the	the	DET
iajs-3245	125	5	value	value	NOUN
iajs-3245	125	6	of	of	ADP
iajs-3245	125	7	auc	auc	NOUN
iajs-3245	125	8	for	for	ADP
iajs-3245	125	9	bayesian	bayesian	NOUN
iajs-3245	125	10	logistics	logistic	NOUN
iajs-3245	125	11	is	be	AUX
iajs-3245	125	12	higher	high	ADJ
iajs-3245	125	13	than	than	ADP
iajs-3245	125	14	classical	classical	ADJ
iajs-3245	125	15	logistic	logistic	NOUN
iajs-3245	125	16	.	.	PUNCT
iajs-3245	126	1	in	in	ADP
iajs-3245	126	2	general	general	ADJ
iajs-3245	126	3	,	,	PUNCT
iajs-3245	126	4	the	the	DET
iajs-3245	126	5	best	good	ADJ
iajs-3245	126	6	model	model	NOUN
iajs-3245	126	7	exists	exist	VERB
iajs-3245	126	8	with	with	ADP
iajs-3245	126	9	a	a	DET
iajs-3245	126	10	higher	high	ADJ
iajs-3245	126	11	auc	auc	NOUN
iajs-3245	126	12	.	.	PUNCT
iajs-3245	127	1	for	for	ADP
iajs-3245	127	2	example	example	NOUN
iajs-3245	127	3	,	,	PUNCT
iajs-3245	127	4	the	the	DET
iajs-3245	127	5	accuracy	accuracy	NOUN
iajs-3245	127	6	in	in	ADP
iajs-3245	127	7	the	the	DET
iajs-3245	127	8	testing	testing	NOUN
iajs-3245	127	9	dataset	dataset	VERB
iajs-3245	127	10	in	in	ADP
iajs-3245	127	11	bayesian	bayesian	NOUN
iajs-3245	127	12	logistic	logistic	NOUN
iajs-3245	127	13	is	be	AUX
iajs-3245	127	14	90.92	90.92	NUM
iajs-3245	127	15	%	%	NOUN
iajs-3245	127	16	.	.	PUNCT
iajs-3245	128	1	the	the	DET
iajs-3245	128	2	model	model	NOUN
iajs-3245	128	3	can	can	AUX
iajs-3245	128	4	discriminate	discriminate	VERB
iajs-3245	128	5	between	between	ADP
iajs-3245	128	6	individuals	individual	NOUN
iajs-3245	128	7	(	(	PUNCT
iajs-3245	128	8	patients	patient	NOUN
iajs-3245	128	9	)	)	PUNCT
iajs-3245	128	10	with	with	ADP
iajs-3245	128	11	heart	heart	NOUN
iajs-3245	128	12	disease	disease	NOUN
iajs-3245	128	13	and	and	CCONJ
iajs-3245	128	14	no	no	DET
iajs-3245	128	15	heart	heart	NOUN
iajs-3245	128	16	disease	disease	NOUN
iajs-3245	128	17	with	with	ADP
iajs-3245	128	18	an	an	DET
iajs-3245	128	19	excellent	excellent	ADJ
iajs-3245	128	20	prospect	prospect	NOUN
iajs-3245	128	21	.	.	PUNCT
iajs-3245	129	1	in	in	ADP
iajs-3245	129	2	general	general	ADJ
iajs-3245	129	3	,	,	PUNCT
iajs-3245	129	4	the	the	DET
iajs-3245	129	5	receiver	receiver	NOUN
iajs-3245	129	6	operating	operate	VERB
iajs-3245	129	7	characteristic	characteristic	NOUN
iajs-3245	129	8	(	(	PUNCT
iajs-3245	129	9	roc	roc	PROPN
iajs-3245	129	10	)	)	PUNCT
iajs-3245	129	11	curve	curve	NOUN
iajs-3245	129	12	with	with	ADP
iajs-3245	129	13	a	a	DET
iajs-3245	129	14	level	level	NOUN
iajs-3245	129	15	of	of	ADP
iajs-3245	129	16	0.7	0.7	NUM
iajs-3245	129	17	appears	appear	VERB
iajs-3245	129	18	to	to	PART
iajs-3245	129	19	be	be	AUX
iajs-3245	129	20	very	very	ADV
iajs-3245	129	21	good	good	ADJ
iajs-3245	129	22	,	,	PUNCT
iajs-3245	129	23	and	and	CCONJ
iajs-3245	129	24	hence	hence	ADV
iajs-3245	129	25	the	the	DET
iajs-3245	129	26	true	true	ADJ
iajs-3245	129	27	positives	positive	NOUN
iajs-3245	129	28	are	be	AUX
iajs-3245	129	29	maximized	maximize	VERB
iajs-3245	129	30	.	.	PUNCT
iajs-3245	130	1	the	the	DET
iajs-3245	130	2	highest	high	ADJ
iajs-3245	130	3	quantity	quantity	NOUN
iajs-3245	130	4	of	of	ADP
iajs-3245	130	5	patients	patient	NOUN
iajs-3245	130	6	with	with	ADP
iajs-3245	130	7	disease	disease	NOUN
iajs-3245	130	8	is	be	AUX
iajs-3245	130	9	not	not	PART
iajs-3245	130	10	recognized	recognize	VERB
iajs-3245	130	11	as	as	ADV
iajs-3245	130	12	well	well	ADV
iajs-3245	130	13	.	.	PUNCT
iajs-3245	131	1	the	the	PRON
iajs-3245	131	2	higher	high	ADJ
iajs-3245	131	3	the	the	DET
iajs-3245	131	4	auc	auc	NOUN
iajs-3245	131	5	,	,	PUNCT
iajs-3245	131	6	the	the	PRON
iajs-3245	131	7	more	more	ADV
iajs-3245	131	8	the	the	DET
iajs-3245	131	9	model	model	NOUN
iajs-3245	131	10	distinguishes	distinguish	VERB
iajs-3245	131	11	between	between	ADP
iajs-3245	131	12	people	people	NOUN
iajs-3245	131	13	who	who	PRON
iajs-3245	131	14	have	have	VERB
iajs-3245	131	15	the	the	DET
iajs-3245	131	16	disease	disease	NOUN
iajs-3245	131	17	and	and	CCONJ
iajs-3245	131	18	others	other	NOUN
iajs-3245	131	19	who	who	PRON
iajs-3245	131	20	do	do	VERB
iajs-3245	131	21	not	not	PART
iajs-3245	131	22	.	.	PUNCT
iajs-3245	132	1	figure	figure	VERB
iajs-3245	132	2	4	4	NUM
iajs-3245	132	3	.	.	PUNCT
iajs-3245	133	1	shows	show	VERB
iajs-3245	133	2	a	a	DET
iajs-3245	133	3	comparison	comparison	NOUN
iajs-3245	133	4	between	between	ADP
iajs-3245	133	5	bayesian	bayesian	NOUN
iajs-3245	133	6	and	and	CCONJ
iajs-3245	133	7	classical	classical	ADJ
iajs-3245	133	8	logistic	logistic	ADJ
iajs-3245	133	9	classifiers	classifier	NOUN
iajs-3245	133	10	.	.	PUNCT
iajs-3245	134	1	overall	overall	ADV
iajs-3245	134	2	,	,	PUNCT
iajs-3245	134	3	the	the	DET
iajs-3245	134	4	bayesian	bayesian	NOUN
iajs-3245	134	5	logistic	logistic	NOUN
iajs-3245	134	6	classifier	classifier	NOUN
iajs-3245	134	7	performs	perform	VERB
iajs-3245	134	8	better	well	ADJ
iajs-3245	134	9	than	than	ADP
iajs-3245	134	10	classical	classical	ADJ
iajs-3245	134	11	logistic	logistic	NOUN
iajs-3245	134	12	.	.	PUNCT
iajs-3245	135	1	by	by	ADP
iajs-3245	135	2	allowing	allow	VERB
iajs-3245	135	3	only	only	ADV
iajs-3245	135	4	a	a	DET
iajs-3245	135	5	few	few	ADJ
iajs-3245	135	6	samples	sample	NOUN
iajs-3245	135	7	(	(	PUNCT
iajs-3245	135	8	less	less	ADJ
iajs-3245	135	9	than	than	ADP
iajs-3245	135	10	100	100	NUM
iajs-3245	135	11	)	)	PUNCT
iajs-3245	135	12	,	,	PUNCT
iajs-3245	135	13	the	the	DET
iajs-3245	135	14	steep	steep	ADJ
iajs-3245	135	15	slopes	slope	NOUN
iajs-3245	135	16	of	of	ADP
iajs-3245	135	17	the	the	DET
iajs-3245	135	18	recall	recall	NOUN
iajs-3245	135	19	curves	curve	NOUN
iajs-3245	135	20	for	for	ADP
iajs-3245	135	21	the	the	DET
iajs-3245	135	22	point	point	NOUN
iajs-3245	135	23	estimate	estimate	NOUN
iajs-3245	135	24	classifiers	classifier	NOUN
iajs-3245	135	25	(	(	PUNCT
iajs-3245	135	26	top	top	ADJ
iajs-3245	135	27	row	row	NOUN
iajs-3245	135	28	,	,	PUNCT
iajs-3245	135	29	red	red	ADJ
iajs-3245	135	30	lines	line	NOUN
iajs-3245	135	31	)	)	PUNCT
iajs-3245	135	32	suggest	suggest	VERB
iajs-3245	135	33	that	that	SCONJ
iajs-3245	135	34	they	they	PRON
iajs-3245	135	35	better	well	ADV
iajs-3245	135	36	identify	identify	VERB
iajs-3245	135	37	patients	patient	NOUN
iajs-3245	135	38	than	than	ADP
iajs-3245	135	39	the	the	DET
iajs-3245	135	40	classical	classical	ADJ
iajs-3245	135	41	logistic	logistic	ADJ
iajs-3245	135	42	classifier	classifier	NOUN
iajs-3245	135	43	.	.	PUNCT
iajs-3245	136	1	a	a	DET
iajs-3245	136	2	smaller	small	ADJ
iajs-3245	136	3	slope	slope	NOUN
iajs-3245	136	4	indicates	indicate	VERB
iajs-3245	136	5	less	less	ADJ
iajs-3245	136	6	progress	progress	NOUN
iajs-3245	136	7	in	in	ADP
iajs-3245	136	8	performance	performance	NOUN
iajs-3245	136	9	as	as	SCONJ
iajs-3245	136	10	more	more	ADJ
iajs-3245	136	11	patients	patient	NOUN
iajs-3245	136	12	are	be	AUX
iajs-3245	136	13	included	include	VERB
iajs-3245	136	14	.	.	PUNCT
iajs-3245	137	1	these	these	DET
iajs-3245	137	2	trends	trend	NOUN
iajs-3245	137	3	are	be	AUX
iajs-3245	137	4	also	also	ADV
iajs-3245	137	5	reflected	reflect	VERB
iajs-3245	137	6	in	in	ADP
iajs-3245	137	7	the	the	DET
iajs-3245	137	8	overall	overall	ADJ
iajs-3245	137	9	measurement	measurement	NOUN
iajs-3245	137	10	of	of	ADP
iajs-3245	137	11	balanced	balanced	ADJ
iajs-3245	137	12	accuracy	accuracy	NOUN
iajs-3245	137	13	.	.	PUNCT
iajs-3245	138	1	figure	figure	NOUN
iajs-3245	138	2	4	4	NUM
iajs-3245	138	3	.	.	PUNCT
iajs-3245	138	4	accuracy	accuracy	NOUN
iajs-3245	138	5	curve	curve	NOUN
iajs-3245	138	6	for	for	ADP
iajs-3245	138	7	both	both	CCONJ
iajs-3245	138	8	classical	classical	ADJ
iajs-3245	138	9	and	and	CCONJ
iajs-3245	138	10	bayesian	bayesian	ADJ
iajs-3245	138	11	logistic	logistic	NOUN
iajs-3245	138	12	.	.	PUNCT
iajs-3245	139	1	classical	classical	ADJ
iajs-3245	139	2	logistic	logistic	NOUN
iajs-3245	139	3	vs	vs	ADP
iajs-3245	139	4	bayesian	bayesian	NOUN
iajs-3245	139	5	logistic	logistic	ADJ
iajs-3245	139	6	false	false	ADJ
iajs-3245	139	7	positive	positive	ADJ
iajs-3245	139	8	rate	rate	NOUN
iajs-3245	139	9	t	t	PROPN
iajs-3245	139	10	ru	ru	PROPN
iajs-3245	140	1	e	e	PROPN
iajs-3245	140	2	p	p	NOUN
iajs-3245	140	3	o	o	X
iajs-3245	140	4	s	s	VERB
iajs-3245	140	5	it	it	PRON
iajs-3245	140	6	iv	iv	ADP
iajs-3245	140	7	e	e	NOUN
iajs-3245	140	8	r	r	NOUN
iajs-3245	140	9	a	a	DET
iajs-3245	140	10	te	te	PROPN
iajs-3245	140	11	0.0	0.0	NUM
iajs-3245	140	12	0.2	0.2	NUM
iajs-3245	140	13	0.4	0.4	NUM
iajs-3245	140	14	0.6	0.6	NUM
iajs-3245	140	15	0.8	0.8	NUM
iajs-3245	140	16	1.0	1.0	NUM
iajs-3245	140	17	0	0	NUM
iajs-3245	140	18	.0	.0	NUM
iajs-3245	140	19	0	0	NUM
iajs-3245	141	1	.2	.2	NUM
iajs-3245	141	2	0	0	NUM
iajs-3245	142	1	.4	.4	NUM
iajs-3245	142	2	0	0	NUM
iajs-3245	143	1	.6	.6	NUM
iajs-3245	143	2	0	0	NUM
iajs-3245	143	3	.8	.8	NUM
iajs-3245	143	4	1	1	NUM
iajs-3245	143	5	.0	.0	NUM
iajs-3245	143	6	bayesian	bayesian	NOUN
iajs-3245	143	7	logistic	logistic	ADJ
iajs-3245	143	8	classical	classical	ADJ
iajs-3245	143	9	logistic	logistic	ADJ
iajs-3245	143	10	ihjpas	ihjpa	NOUN
iajs-3245	143	11	.	.	PUNCT
iajs-3245	144	1	2024	2024	NUM
iajs-3245	144	2	,	,	PUNCT
iajs-3245	144	3	37(4	37(4	PRON
iajs-3245	144	4	)	)	PUNCT
iajs-3245	144	5	399	399	NUM
iajs-3245	144	6	7	7	NUM
iajs-3245	144	7	.	.	PUNCT
iajs-3245	144	8	conclusion	conclusion	NOUN
iajs-3245	144	9	the	the	DET
iajs-3245	144	10	bayesian	bayesian	NOUN
iajs-3245	144	11	markov	markov	NOUN
iajs-3245	144	12	chain	chain	NOUN
iajs-3245	144	13	monte	monte	PROPN
iajs-3245	144	14	carlo	carlo	PROPN
iajs-3245	144	15	(	(	PUNCT
iajs-3245	144	16	mcmc	mcmc	PROPN
iajs-3245	144	17	)	)	PUNCT
iajs-3245	144	18	technique	technique	NOUN
iajs-3245	144	19	is	be	AUX
iajs-3245	144	20	presented	present	VERB
iajs-3245	144	21	in	in	ADP
iajs-3245	144	22	this	this	DET
iajs-3245	144	23	article	article	NOUN
iajs-3245	144	24	as	as	ADP
iajs-3245	144	25	an	an	DET
iajs-3245	144	26	alternative	alternative	ADJ
iajs-3245	144	27	method	method	NOUN
iajs-3245	144	28	for	for	ADP
iajs-3245	144	29	estimating	estimate	VERB
iajs-3245	144	30	the	the	DET
iajs-3245	144	31	logistic	logistic	ADJ
iajs-3245	144	32	regression	regression	NOUN
iajs-3245	144	33	model	model	NOUN
iajs-3245	144	34	.	.	PUNCT
iajs-3245	145	1	we	we	PRON
iajs-3245	145	2	derived	derive	VERB
iajs-3245	145	3	a	a	DET
iajs-3245	145	4	bayesian	bayesian	NOUN
iajs-3245	145	5	logistic	logistic	ADJ
iajs-3245	145	6	model	model	NOUN
iajs-3245	145	7	by	by	ADP
iajs-3245	145	8	using	use	VERB
iajs-3245	145	9	a	a	DET
iajs-3245	145	10	particular	particular	ADJ
iajs-3245	145	11	type	type	NOUN
iajs-3245	145	12	of	of	ADP
iajs-3245	145	13	mcmc	mcmc	PROPN
iajs-3245	145	14	,	,	PUNCT
iajs-3245	145	15	the	the	DET
iajs-3245	145	16	metropolis	metropolis	PROPN
iajs-3245	145	17	-	-	PUNCT
iajs-3245	145	18	hastings	hastings	PROPN
iajs-3245	145	19	(	(	PUNCT
iajs-3245	145	20	mh	mh	PROPN
iajs-3245	145	21	)	)	PUNCT
iajs-3245	145	22	algorithm	algorithm	NOUN
iajs-3245	145	23	.	.	PUNCT
iajs-3245	146	1	the	the	DET
iajs-3245	146	2	mh	mh	PROPN
iajs-3245	146	3	algorithm	algorithm	PROPN
iajs-3245	146	4	was	be	AUX
iajs-3245	146	5	introduced	introduce	VERB
iajs-3245	146	6	and	and	CCONJ
iajs-3245	146	7	used	use	VERB
iajs-3245	146	8	to	to	PART
iajs-3245	146	9	obtain	obtain	VERB
iajs-3245	146	10	the	the	DET
iajs-3245	146	11	estimated	estimate	VERB
iajs-3245	146	12	parameters	parameter	NOUN
iajs-3245	146	13	in	in	ADP
iajs-3245	146	14	the	the	DET
iajs-3245	146	15	new	new	ADJ
iajs-3245	146	16	method	method	NOUN
iajs-3245	146	17	.	.	PUNCT
iajs-3245	147	1	the	the	DET
iajs-3245	147	2	bayesian	bayesian	NOUN
iajs-3245	147	3	logistic	logistic	ADJ
iajs-3245	147	4	model	model	NOUN
iajs-3245	147	5	was	be	AUX
iajs-3245	147	6	used	use	VERB
iajs-3245	147	7	to	to	PART
iajs-3245	147	8	overcome	overcome	VERB
iajs-3245	147	9	some	some	DET
iajs-3245	147	10	limitations	limitation	NOUN
iajs-3245	147	11	of	of	ADP
iajs-3245	147	12	the	the	DET
iajs-3245	147	13	classical	classical	ADJ
iajs-3245	147	14	logistic	logistic	ADJ
iajs-3245	147	15	model	model	NOUN
iajs-3245	147	16	.	.	PUNCT
iajs-3245	148	1	the	the	DET
iajs-3245	148	2	actual	actual	ADJ
iajs-3245	148	3	data	datum	NOUN
iajs-3245	148	4	of	of	ADP
iajs-3245	148	5	the	the	DET
iajs-3245	148	6	heart	heart	NOUN
iajs-3245	148	7	dataset	dataset	NOUN
iajs-3245	148	8	is	be	AUX
iajs-3245	148	9	used	use	VERB
iajs-3245	148	10	in	in	ADP
iajs-3245	148	11	this	this	DET
iajs-3245	148	12	paper	paper	NOUN
iajs-3245	148	13	to	to	PART
iajs-3245	148	14	classify	classify	VERB
iajs-3245	148	15	healthy	healthy	ADJ
iajs-3245	148	16	and	and	CCONJ
iajs-3245	148	17	non	non	ADJ
iajs-3245	148	18	-	-	ADJ
iajs-3245	148	19	healthy	healthy	ADJ
iajs-3245	148	20	people	people	NOUN
iajs-3245	148	21	.	.	PUNCT
iajs-3245	149	1	the	the	DET
iajs-3245	149	2	modified	modify	VERB
iajs-3245	149	3	method	method	NOUN
iajs-3245	149	4	is	be	AUX
iajs-3245	149	5	compared	compare	VERB
iajs-3245	149	6	with	with	ADP
iajs-3245	149	7	the	the	DET
iajs-3245	149	8	classical	classical	ADJ
iajs-3245	149	9	logistic	logistic	NOUN
iajs-3245	149	10	,	,	PUNCT
iajs-3245	149	11	svm	svm	NOUN
iajs-3245	149	12	and	and	CCONJ
iajs-3245	149	13	ann	ann	PROPN
iajs-3245	149	14	.	.	PUNCT
iajs-3245	150	1	it	it	PRON
iajs-3245	150	2	is	be	AUX
iajs-3245	150	3	shown	show	VERB
iajs-3245	150	4	that	that	SCONJ
iajs-3245	150	5	our	our	PRON
iajs-3245	150	6	method	method	NOUN
iajs-3245	150	7	performs	perform	VERB
iajs-3245	150	8	better	well	ADJ
iajs-3245	150	9	than	than	ADP
iajs-3245	150	10	other	other	ADJ
iajs-3245	150	11	methods	method	NOUN
iajs-3245	150	12	and	and	CCONJ
iajs-3245	150	13	takes	take	VERB
iajs-3245	150	14	less	less	ADJ
iajs-3245	150	15	time	time	NOUN
iajs-3245	150	16	.	.	PUNCT
iajs-3245	151	1	acknowledgment	acknowledgment	NOUN
iajs-3245	151	2	the	the	DET
iajs-3245	151	3	authors	author	NOUN
iajs-3245	151	4	are	be	AUX
iajs-3245	151	5	very	very	ADV
iajs-3245	151	6	grateful	grateful	ADJ
iajs-3245	151	7	to	to	ADP
iajs-3245	151	8	the	the	DET
iajs-3245	151	9	executive	executive	ADJ
iajs-3245	151	10	manager	manager	NOUN
iajs-3245	151	11	and	and	CCONJ
iajs-3245	151	12	editorial	editorial	PROPN
iajs-3245	151	13	board	board	NOUN
iajs-3245	151	14	members	member	NOUN
iajs-3245	151	15	of	of	ADP
iajs-3245	151	16	the	the	DET
iajs-3245	151	17	college	college	PROPN
iajs-3245	151	18	of	of	ADP
iajs-3245	151	19	administration	administration	PROPN
iajs-3245	151	20	and	and	CCONJ
iajs-3245	151	21	economics	economic	NOUN
iajs-3245	151	22	conflict	conflict	NOUN
iajs-3245	151	23	of	of	ADP
iajs-3245	151	24	interest	interest	NOUN
iajs-3245	151	25	the	the	DET
iajs-3245	151	26	authors	author	NOUN
iajs-3245	151	27	declare	declare	VERB
iajs-3245	151	28	that	that	SCONJ
iajs-3245	151	29	they	they	PRON
iajs-3245	151	30	have	have	VERB
iajs-3245	151	31	no	no	DET
iajs-3245	151	32	conflicts	conflict	NOUN
iajs-3245	151	33	of	of	ADP
iajs-3245	151	34	interest	interest	NOUN
iajs-3245	151	35	.	.	PUNCT
iajs-3245	152	1	funding	funding	NOUN
iajs-3245	152	2	there	there	PRON
iajs-3245	152	3	is	be	VERB
iajs-3245	152	4	no	no	DET
iajs-3245	152	5	funding	funding	NOUN
iajs-3245	152	6	for	for	ADP
iajs-3245	152	7	the	the	DET
iajs-3245	152	8	article	article	NOUN
iajs-3245	152	9	.	.	PUNCT
iajs-3245	153	1	references	reference	NOUN
iajs-3245	153	2	1	1	NUM
iajs-3245	153	3	.	.	X
iajs-3245	153	4	zhang	zhang	PROPN
iajs-3245	153	5	,	,	PUNCT
iajs-3245	153	6	l.	l.	PROPN
iajs-3245	153	7	;	;	PUNCT
iajs-3245	153	8	dong	dong	PROPN
iajs-3245	153	9	,	,	PUNCT
iajs-3245	153	10	l.	l.	PROPN
iajs-3245	153	11	;	;	PUNCT
iajs-3245	153	12	cheng	cheng	PROPN
iajs-3245	153	13	,	,	PUNCT
iajs-3245	153	14	s.	s.	PROPN
iajs-3245	153	15	;	;	PUNCT
iajs-3245	153	16	li	li	PROPN
iajs-3245	153	17	,	,	PUNCT
iajs-3245	153	18	w.	w.	PROPN
iajs-3245	153	19	;	;	PUNCT
iajs-3245	153	20	wang	wang	PROPN
iajs-3245	153	21	,	,	PUNCT
iajs-3245	153	22	b.	b.	PROPN
iajs-3245	153	23	;	;	PUNCT
iajs-3245	153	24	liu	liu	PROPN
iajs-3245	153	25	,	,	PUNCT
iajs-3245	153	26	h.	h.	PROPN
iajs-3245	153	27	;	;	PUNCT
iajs-3245	153	28	chen	chen	PROPN
iajs-3245	153	29	,	,	PUNCT
iajs-3245	153	30	k.	k.	PROPN
iajs-3245	153	31	efficient	efficient	ADJ
iajs-3245	153	32	reliability	reliability	NOUN
iajs-3245	153	33	assessment	assessment	NOUN
iajs-3245	153	34	method	method	NOUN
iajs-3245	153	35	for	for	ADP
iajs-3245	153	36	bridges	bridge	NOUN
iajs-3245	153	37	based	base	VERB
iajs-3245	153	38	on	on	ADP
iajs-3245	153	39	markov	markov	NOUN
iajs-3245	153	40	chain	chain	NOUN
iajs-3245	153	41	monte	monte	PROPN
iajs-3245	153	42	carlo	carlo	PROPN
iajs-3245	153	43	(	(	PUNCT
iajs-3245	153	44	mcmc	mcmc	PROPN
iajs-3245	153	45	)	)	PUNCT
iajs-3245	153	46	with	with	ADP
iajs-3245	153	47	metropolis	metropolis	PROPN
iajs-3245	153	48	-	-	PUNCT
iajs-3245	153	49	hasting	hasting	PROPN
iajs-3245	153	50	algorithm	algorithm	PROPN
iajs-3245	153	51	(	(	PUNCT
iajs-3245	153	52	mha	mha	NOUN
iajs-3245	153	53	)	)	PUNCT
iajs-3245	153	54	.	.	PUNCT
iajs-3245	154	1	in	in	ADP
iajs-3245	154	2	iop	iop	PROPN
iajs-3245	154	3	conference	conference	NOUN
iajs-3245	154	4	series	series	NOUN
iajs-3245	154	5	:	:	PUNCT
iajs-3245	154	6	earth	earth	NOUN
iajs-3245	154	7	and	and	CCONJ
iajs-3245	154	8	environmental	environmental	ADJ
iajs-3245	154	9	science	science	NOUN
iajs-3245	154	10	2020	2020	NUM
iajs-3245	154	11	,	,	PUNCT
iajs-3245	154	12	580(1	580(1	NUM
iajs-3245	154	13	)	)	PUNCT
iajs-3245	154	14	,	,	PUNCT
iajs-3245	154	15	012030	012030	NUM
iajs-3245	154	16	.	.	PUNCT
iajs-3245	155	1	https://doi.org/10.1088/1755-1315/580/1/012030	https://doi.org/10.1088/1755-1315/580/1/012030	X
iajs-3245	155	2	2	2	X
iajs-3245	155	3	.	.	PUNCT
iajs-3245	155	4	mahdi	mahdi	PROPN
iajs-3245	155	5	gj	gj	PROPN
iajs-3245	155	6	,	,	PUNCT
iajs-3245	155	7	kalaf	kalaf	PROPN
iajs-3245	155	8	ba	ba	PROPN
iajs-3245	155	9	,	,	PUNCT
iajs-3245	155	10	khaleel	khaleel	PROPN
iajs-3245	155	11	ma	ma	PROPN
iajs-3245	155	12	.	.	PROPN
iajs-3245	155	13	enhanced	enhance	VERB
iajs-3245	155	14	supervised	supervised	ADJ
iajs-3245	155	15	principal	principal	ADJ
iajs-3245	155	16	component	component	NOUN
iajs-3245	155	17	analysis	analysis	NOUN
iajs-3245	155	18	for	for	ADP
iajs-3245	155	19	cancer	cancer	NOUN
iajs-3245	155	20	classification	classification	NOUN
iajs-3245	155	21	.	.	PUNCT
iajs-3245	156	1	iraqi	iraqi	ADJ
iajs-3245	156	2	journal	journal	PROPN
iajs-3245	156	3	of	of	ADP
iajs-3245	156	4	science	science	NOUN
iajs-3245	156	5	2021	2021	NUM
iajs-3245	156	6	,	,	PUNCT
iajs-3245	156	7	62(4	62(4	NUM
iajs-3245	156	8	)	)	PUNCT
iajs-3245	156	9	,	,	PUNCT
iajs-3245	156	10	1321	1321	NUM
iajs-3245	156	11	-	-	SYM
iajs-3245	156	12	1333	1333	NUM
iajs-3245	156	13	.	.	PUNCT
iajs-3245	157	1	https://doi.org/10.24996/ijs.2021.62.4.28	https://doi.org/10.24996/ijs.2021.62.4.28	PROPN
iajs-3245	157	2	3	3	NUM
iajs-3245	157	3	.	.	PUNCT
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iajs-3245	157	5	t	t	PROPN
iajs-3245	157	6	;	;	PUNCT
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iajs-3245	157	8	v.	v.	PROPN
iajs-3245	157	9	metropolis	metropolis	PROPN
iajs-3245	157	10	–	–	PUNCT
iajs-3245	157	11	hastings	hastings	PROPN
iajs-3245	157	12	via	via	ADP
iajs-3245	157	13	classification	classification	NOUN
iajs-3245	157	14	.	.	PUNCT
iajs-3245	158	1	journal	journal	NOUN
iajs-3245	158	2	of	of	ADP
iajs-3245	158	3	the	the	DET
iajs-3245	158	4	american	american	PROPN
iajs-3245	158	5	statistical	statistical	ADJ
iajs-3245	158	6	association	association	NOUN
iajs-3245	158	7	.	.	PUNCT
iajs-3245	159	1	2023	2023	NUM
iajs-3245	159	2	,	,	PUNCT
iajs-3245	159	3	118(544	118(544	NUM
iajs-3245	159	4	)	)	PUNCT
iajs-3245	159	5	,	,	PUNCT
iajs-3245	159	6	2533	2533	NUM
iajs-3245	159	7	-	-	SYM
iajs-3245	159	8	2547	2547	NUM
iajs-3245	159	9	.	.	PUNCT
iajs-3245	160	1	https://doi.org/10.1080/01621459.2022.2060836	https://doi.org/10.1080/01621459.2022.2060836	NOUN
iajs-3245	161	1	4	4	X
iajs-3245	161	2	.	.	X
iajs-3245	161	3	sur	sur	PROPN
iajs-3245	161	4	,	,	PUNCT
iajs-3245	161	5	p.	p.	NOUN
iajs-3245	161	6	;	;	PUNCT
iajs-3245	161	7	candès	candès	NOUN
iajs-3245	161	8	,	,	PUNCT
iajs-3245	161	9	ej	ej	PROPN
iajs-3245	161	10	.	.	PUNCT
iajs-3245	162	1	a	a	DET
iajs-3245	162	2	modern	modern	ADJ
iajs-3245	162	3	maximum	maximum	ADJ
iajs-3245	162	4	-	-	PUNCT
iajs-3245	162	5	likelihood	likelihood	NOUN
iajs-3245	162	6	theory	theory	NOUN
iajs-3245	162	7	for	for	ADP
iajs-3245	162	8	high	high	ADJ
iajs-3245	162	9	-	-	PUNCT
iajs-3245	162	10	dimensional	dimensional	ADJ
iajs-3245	162	11	logistic	logistic	ADJ
iajs-3245	162	12	regression	regression	NOUN
iajs-3245	162	13	.	.	PUNCT
iajs-3245	163	1	proceedings	proceeding	NOUN
iajs-3245	163	2	of	of	ADP
iajs-3245	163	3	the	the	DET
iajs-3245	163	4	national	national	PROPN
iajs-3245	163	5	academy	academy	PROPN
iajs-3245	163	6	of	of	ADP
iajs-3245	163	7	sciences	sciences	PROPN
iajs-3245	163	8	2019	2019	NUM
iajs-3245	163	9	,	,	PUNCT
iajs-3245	163	10	116(29	116(29	NUM
iajs-3245	163	11	)	)	PUNCT
iajs-3245	163	12	,	,	PUNCT
iajs-3245	163	13	14516	14516	NUM
iajs-3245	163	14	-	-	SYM
iajs-3245	163	15	25	25	NUM
iajs-3245	163	16	.	.	PUNCT
iajs-3245	163	17	https://doi.org/10.1073/pnas.1907936116	https://doi.org/10.1073/pnas.1907936116	ADJ
iajs-3245	163	18	5	5	NUM
iajs-3245	163	19	.	.	X
iajs-3245	163	20	belenguer	belenguer	NOUN
iajs-3245	163	21	-	-	PUNCT
iajs-3245	163	22	llorens	llorens	PROPN
iajs-3245	163	23	,	,	PUNCT
iajs-3245	163	24	a.	a.	NOUN
iajs-3245	163	25	;	;	PUNCT
iajs-3245	163	26	sevilla	sevilla	NOUN
iajs-3245	163	27	-	-	PUNCT
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iajs-3245	163	29	,	,	PUNCT
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iajs-3245	163	31	;	;	PUNCT
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iajs-3245	163	33	,	,	PUNCT
iajs-3245	163	34	m.	m.	NOUN
iajs-3245	163	35	;	;	PUNCT
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iajs-3245	163	37	-	-	PUNCT
iajs-3245	163	38	montenegro	montenegro	PROPN
iajs-3245	163	39	,	,	PUNCT
iajs-3245	163	40	m.l	m.l	PROPN
iajs-3245	163	41	.	.	PROPN
iajs-3245	163	42	;	;	PUNCT
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iajs-3245	163	44	-	-	PUNCT
iajs-3245	163	45	verdejo	verdejo	NOUN
iajs-3245	163	46	,	,	PUNCT
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iajs-3245	163	48	a	a	DET
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iajs-3245	163	50	bayesian	bayesian	NOUN
iajs-3245	163	51	linear	linear	NOUN
iajs-3245	163	52	regression	regression	NOUN
iajs-3245	163	53	model	model	NOUN
iajs-3245	163	54	for	for	ADP
iajs-3245	163	55	the	the	DET
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iajs-3245	163	57	of	of	ADP
iajs-3245	163	58	neuroimaging	neuroimage	VERB
iajs-3245	163	59	data	datum	NOUN
iajs-3245	163	60	.	.	PUNCT
iajs-3245	164	1	applied	apply	VERB
iajs-3245	164	2	science	science	NOUN
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iajs-3245	164	4	,	,	PUNCT
iajs-3245	164	5	12(5	12(5	NUM
iajs-3245	164	6	)	)	PUNCT
iajs-3245	164	7	,	,	PUNCT
iajs-3245	164	8	2571	2571	NUM
iajs-3245	164	9	.	.	PUNCT
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iajs-3245	165	3	.	.	PUNCT
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iajs-3245	166	2	,	,	PUNCT
iajs-3245	166	3	w.	w.	PROPN
iajs-3245	166	4	;	;	PUNCT
iajs-3245	166	5	francelino	francelino	PROPN
iajs-3245	166	6	,	,	PUNCT
iajs-3245	166	7	m.r	m.r	PROPN
iajs-3245	166	8	.	.	PROPN
iajs-3245	166	9	;	;	PUNCT
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iajs-3245	166	11	,	,	PUNCT
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iajs-3245	166	13	.	.	PROPN
iajs-3245	166	14	;	;	PUNCT
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iajs-3245	166	17	,	,	PUNCT
iajs-3245	166	18	e.i	e.i	PROPN
iajs-3245	166	19	.	.	PROPN
iajs-3245	166	20	;	;	PUNCT
iajs-3245	166	21	rocha	rocha	PROPN
iajs-3245	166	22	,	,	PUNCT
iajs-3245	166	23	g.c	g.c	PROPN
iajs-3245	166	24	.	.	PROPN
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iajs-3245	166	26	logistic	logistic	ADJ
iajs-3245	166	27	regression	regression	NOUN
iajs-3245	166	28	and	and	CCONJ
iajs-3245	166	29	random	random	ADJ
iajs-3245	166	30	forest	forest	NOUN
iajs-3245	166	31	classifiers	classifier	NOUN
iajs-3245	166	32	in	in	ADP
iajs-3245	166	33	digital	digital	ADJ
iajs-3245	166	34	mapping	mapping	NOUN
iajs-3245	166	35	of	of	ADP
iajs-3245	166	36	soil	soil	NOUN
iajs-3245	166	37	classes	class	NOUN
iajs-3245	166	38	in	in	ADP
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iajs-3245	166	41	.	.	PUNCT
iajs-3245	167	1	revista	revista	PROPN
iajs-3245	167	2	brasileira	brasileira	PROPN
iajs-3245	167	3	de	de	PROPN
iajs-3245	167	4	ciência	ciência	PROPN
iajs-3245	167	5	do	do	VERB
iajs-3245	167	6	solo	solo	PROPN
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iajs-3245	167	8	,	,	PUNCT
iajs-3245	167	9	42	42	NUM
iajs-3245	167	10	,	,	PUNCT
iajs-3245	167	11	e0170133	e0170133	PROPN
iajs-3245	167	12	.	.	PUNCT
iajs-3245	168	1	https://doi.org/10.1590/18069657rbcs20170133	https://doi.org/10.1590/18069657rbcs20170133	ADV
iajs-3245	168	2	7	7	NUM
iajs-3245	168	3	.	.	PUNCT
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iajs-3245	169	2	,	,	PUNCT
iajs-3245	169	3	y.	y.	PROPN
iajs-3245	169	4	;	;	PUNCT
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iajs-3245	169	6	,	,	PUNCT
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iajs-3245	169	8	.	.	PROPN
iajs-3245	169	9	;	;	PUNCT
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iajs-3245	169	11	,	,	PUNCT
iajs-3245	169	12	z.p	z.p	PROPN
iajs-3245	169	13	.	.	PROPN
iajs-3245	169	14	a	a	DET
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iajs-3245	169	16	for	for	ADP
iajs-3245	169	17	multi	multi	ADJ
iajs-3245	169	18	-	-	ADJ
iajs-3245	169	19	class	class	ADJ
iajs-3245	169	20	sentiment	sentiment	NOUN
iajs-3245	169	21	classification	classification	NOUN
iajs-3245	169	22	based	base	VERB
iajs-3245	169	23	on	on	ADP
iajs-3245	169	24	an	an	DET
iajs-3245	169	25	improved	improved	ADJ
iajs-3245	169	26	one	one	NUM
iajs-3245	169	27	-	-	PUNCT
iajs-3245	169	28	vs	vs	ADP
iajs-3245	169	29	-	-	PUNCT
iajs-3245	169	30	one	one	NUM
iajs-3245	169	31	(	(	PUNCT
iajs-3245	169	32	ovo	ovo	PROPN
iajs-3245	169	33	)	)	PUNCT
iajs-3245	169	34	strategy	strategy	NOUN
iajs-3245	169	35	and	and	CCONJ
iajs-3245	169	36	the	the	DET
iajs-3245	169	37	support	support	NOUN
iajs-3245	169	38	vector	vector	NOUN
iajs-3245	169	39	machine	machine	NOUN
iajs-3245	169	40	(	(	PUNCT
iajs-3245	169	41	svm	svm	ADJ
iajs-3245	169	42	)	)	PUNCT
iajs-3245	169	43	algorithm	algorithm	NOUN
iajs-3245	169	44	.	.	PUNCT
iajs-3245	170	1	information	information	NOUN
iajs-3245	170	2	sciences	sciences	PROPN
iajs-3245	170	3	.	.	PUNCT
iajs-3245	171	1	2017	2017	NUM
iajs-3245	171	2	,	,	PUNCT
iajs-3245	171	3	394	394	NUM
iajs-3245	171	4	,	,	PUNCT
iajs-3245	171	5	38	38	NUM
iajs-3245	171	6	-	-	SYM
iajs-3245	171	7	52	52	NUM
iajs-3245	171	8	.	.	PUNCT
iajs-3245	172	1	https://doi.org/10.1016/j.ins.2017.02.016	https://doi.org/10.1016/j.ins.2017.02.016	PROPN
iajs-3245	172	2	8	8	NUM
iajs-3245	172	3	.	.	PUNCT
iajs-3245	173	1	dwivedi	dwivedi	PROPN
iajs-3245	173	2	,	,	PUNCT
iajs-3245	173	3	ak	ak	PROPN
iajs-3245	173	4	.	.	PROPN
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iajs-3245	173	6	neural	neural	ADJ
iajs-3245	173	7	network	network	NOUN
iajs-3245	173	8	model	model	NOUN
iajs-3245	173	9	for	for	ADP
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iajs-3245	173	12	classification	classification	NOUN
iajs-3245	173	13	using	use	VERB
iajs-3245	173	14	microarray	microarray	NOUN
iajs-3245	173	15	gene	gene	NOUN
iajs-3245	173	16	expression	expression	NOUN
iajs-3245	173	17	data	datum	NOUN
iajs-3245	173	18	.	.	PUNCT
iajs-3245	174	1	neural	neural	ADJ
iajs-3245	174	2	computing	computing	NOUN
iajs-3245	174	3	and	and	CCONJ
iajs-3245	174	4	applications	application	NOUN
iajs-3245	174	5	2018	2018	NUM
iajs-3245	174	6	,	,	PUNCT
iajs-3245	174	7	29(12	29(12	NUM
iajs-3245	174	8	)	)	PUNCT
iajs-3245	174	9	,	,	PUNCT
iajs-3245	174	10	1545	1545	NUM
iajs-3245	174	11	-	-	SYM
iajs-3245	174	12	54	54	NUM
iajs-3245	174	13	.	.	PUNCT
iajs-3245	175	1	https://doi.org/10.1007/s00521-016-2701-1	https://doi.org/10.1007/s00521-016-2701-1	NUM
iajs-3245	175	2	9	9	NUM
iajs-3245	175	3	.	.	PUNCT
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iajs-3245	175	5	,	,	PUNCT
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iajs-3245	176	8	-	-	PUNCT
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iajs-3245	176	10	microarray	microarray	NOUN
iajs-3245	176	11	data	datum	NOUN
iajs-3245	176	12	based	base	VERB
iajs-3245	176	13	on	on	ADP
iajs-3245	176	14	sparse	sparse	ADJ
iajs-3245	176	15	logistic	logistic	ADJ
iajs-3245	176	16	regression	regression	NOUN
iajs-3245	176	17	.	.	PUNCT
iajs-3245	177	1	electronic	electronic	ADJ
iajs-3245	177	2	journal	journal	NOUN
iajs-3245	177	3	of	of	ADP
iajs-3245	177	4	applied	apply	VERB
iajs-3245	177	5	statistical	statistical	ADJ
iajs-3245	177	6	analysis	analysis	NOUN
iajs-3245	177	7	2017	2017	NUM
iajs-3245	177	8	,	,	PUNCT
iajs-3245	177	9	10(1	10(1	NUM
iajs-3245	177	10	)	)	PUNCT
iajs-3245	177	11	,	,	PUNCT
iajs-3245	177	12	242	242	NUM
iajs-3245	177	13	-	-	SYM
iajs-3245	177	14	56	56	NUM
iajs-3245	177	15	.	.	PUNCT
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iajs-3245	178	3	https://doi.org/10.24996/ijs.2021.62.4.28	https://doi.org/10.24996/ijs.2021.62.4.28	X
iajs-3245	178	4	https://doi.org/10.1080/01621459.2022.2060836	https://doi.org/10.1080/01621459.2022.2060836	NOUN
iajs-3245	179	1	https://doi.org/10.1073/pnas.1907936116	https://doi.org/10.1073/pnas.1907936116	ADV
iajs-3245	179	2	https://doi.org/10.1590/18069657rbcs20170133	https://doi.org/10.1590/18069657rbcs20170133	ADV
iajs-3245	179	3	https://doi.org/10.1016/j.ins.2017.02.016	https://doi.org/10.1016/j.ins.2017.02.016	PROPN
iajs-3245	179	4	https://doi.org/10.1285/i20705948v10n1p242	https://doi.org/10.1285/i20705948v10n1p242	PROPN
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iajs-3245	179	6	.	.	PUNCT
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iajs-3245	180	2	,	,	PUNCT
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iajs-3245	180	4	)	)	PUNCT
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iajs-3245	180	7	.	.	PUNCT
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iajs-3245	181	5	;	;	PUNCT
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iajs-3245	182	4	.	.	PROPN
iajs-3245	182	5	;	;	PUNCT
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iajs-3245	182	16	,	,	PUNCT
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iajs-3245	182	22	-	-	PUNCT
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iajs-3245	183	7	,	,	PUNCT
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iajs-3245	183	9	,	,	PUNCT
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iajs-3245	183	11	.	.	PUNCT
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iajs-3245	184	3	.	.	PUNCT
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iajs-3245	185	2	,	,	PUNCT
iajs-3245	185	3	y.	y.	PROPN
iajs-3245	185	4	;	;	PUNCT
iajs-3245	185	5	li	li	PROPN
iajs-3245	185	6	,	,	PUNCT
iajs-3245	185	7	z.	z.	PROPN
iajs-3245	185	8	;	;	PUNCT
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iajs-3245	185	10	,	,	PUNCT
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iajs-3245	185	12	;	;	PUNCT
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iajs-3245	185	14	,	,	PUNCT
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iajs-3245	185	32	tobit	tobit	NOUN
iajs-3245	185	33	model	model	NOUN
iajs-3245	185	34	.	.	PUNCT
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iajs-3245	186	2	analysis	analysis	NOUN
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iajs-3245	186	6	,	,	PUNCT
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iajs-3245	186	8	,	,	PUNCT
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iajs-3245	186	10	-	-	SYM
iajs-3245	186	11	74	74	NUM
iajs-3245	186	12	.	.	PUNCT
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iajs-3245	187	3	.	.	PUNCT
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iajs-3245	188	2	,	,	PUNCT
iajs-3245	188	3	bm	bm	PROPN
iajs-3245	188	4	.	.	PROPN
iajs-3245	188	5	;	;	PUNCT
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iajs-3245	188	7	-	-	PUNCT
iajs-3245	188	8	cancelas	cancelas	PROPN
iajs-3245	188	9	,	,	PUNCT
iajs-3245	188	10	n.	n.	NOUN
iajs-3245	188	11	;	;	PUNCT
iajs-3245	188	12	soler	soler	PROPN
iajs-3245	188	13	-	-	PUNCT
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iajs-3245	188	15	,	,	PUNCT
iajs-3245	188	16	f.	f.	PROPN
iajs-3245	188	17	camarero	camarero	PROPN
iajs-3245	188	18	-	-	PUNCT
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iajs-3245	188	20	,	,	PUNCT
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iajs-3245	188	22	classification	classification	NOUN
iajs-3245	188	23	and	and	CCONJ
iajs-3245	188	24	prediction	prediction	NOUN
iajs-3245	188	25	of	of	ADP
iajs-3245	188	26	port	port	NOUN
iajs-3245	188	27	variables	variable	NOUN
iajs-3245	188	28	using	use	VERB
iajs-3245	188	29	bayesian	bayesian	NOUN
iajs-3245	188	30	networks	network	NOUN
iajs-3245	188	31	.	.	PUNCT
iajs-3245	189	1	transport	transport	NOUN
iajs-3245	189	2	policy	policy	NOUN
iajs-3245	189	3	2018	2018	NUM
iajs-3245	189	4	,	,	PUNCT
iajs-3245	189	5	67	67	NUM
iajs-3245	189	6	,	,	PUNCT
iajs-3245	189	7	57	57	NUM
iajs-3245	189	8	-	-	SYM
iajs-3245	189	9	66	66	NUM
iajs-3245	189	10	.	.	PUNCT
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iajs-3245	190	2	13	13	NUM
iajs-3245	190	3	.	.	PUNCT
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iajs-3245	191	2	,	,	PUNCT
iajs-3245	191	3	zy	zy	PROPN
iajs-3245	191	4	.	.	PROPN
iajs-3245	191	5	;	;	PUNCT
iajs-3245	192	1	alhamzawi	alhamzawi	PROPN
iajs-3245	192	2	,	,	PUNCT
iajs-3245	192	3	r.	r.	PROPN
iajs-3245	192	4	;	;	PUNCT
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iajs-3245	192	6	,	,	PUNCT
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iajs-3245	192	21	.	.	PUNCT
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iajs-3245	193	2	in	in	ADP
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iajs-3245	193	4	and	and	CCONJ
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iajs-3245	193	6	2018	2018	NUM
iajs-3245	193	7	,	,	PUNCT
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iajs-3245	193	9	,	,	PUNCT
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iajs-3245	193	11	-	-	SYM
iajs-3245	193	12	52	52	NUM
iajs-3245	193	13	.	.	PUNCT
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iajs-3245	194	3	.	.	PUNCT
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iajs-3245	195	3	,	,	PUNCT
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iajs-3245	195	6	,	,	PUNCT
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iajs-3245	195	8	-	-	PUNCT
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iajs-3245	196	10	net	net	ADJ
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iajs-3245	196	12	.	.	PUNCT
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iajs-3245	197	9	,	,	PUNCT
iajs-3245	197	10	1879(3	1879(3	NUM
iajs-3245	197	11	)	)	PUNCT
iajs-3245	197	12	,	,	PUNCT
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iajs-3245	197	14	.	.	PUNCT
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iajs-3245	199	2	.	.	PUNCT
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iajs-3245	200	2	a	a	X
iajs-3245	200	3	,	,	PUNCT
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iajs-3245	200	20	.	.	PUNCT
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iajs-3245	201	6	,	,	PUNCT
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iajs-3245	201	8	)	)	PUNCT
iajs-3245	201	9	,	,	PUNCT
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iajs-3245	201	11	-	-	SYM
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iajs-3245	201	13	.	.	PUNCT
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iajs-3245	202	3	.	.	PUNCT
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iajs-3245	203	25	.	.	PUNCT
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iajs-3245	204	6	,	,	PUNCT
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iajs-3245	204	8	.	.	PUNCT
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iajs-3245	205	3	.	.	PUNCT
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iajs-3245	206	24	.	.	PUNCT
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iajs-3245	207	10	.	.	PUNCT
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iajs-3245	208	3	.	.	PUNCT
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iajs-3245	209	8	;	;	PUNCT
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iajs-3245	209	13	-	-	PUNCT
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iajs-3245	210	11	.	.	PUNCT
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iajs-3245	211	3	.	.	PUNCT
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iajs-3245	212	6	,	,	PUNCT
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iajs-3245	212	8	;	;	PUNCT
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iajs-3245	212	10	,	,	PUNCT
iajs-3245	212	11	j.	j.	PROPN
iajs-3245	212	12	a	a	DET
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iajs-3245	212	15	to	to	ADP
iajs-3245	212	16	reliability	reliability	NOUN
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iajs-3245	213	7	,	,	PUNCT
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iajs-3245	213	9	,	,	PUNCT
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iajs-3245	213	11	.	.	PUNCT
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iajs-3245	214	3	.	.	PUNCT
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iajs-3245	215	2	,	,	PUNCT
iajs-3245	215	3	k.	k.	PROPN
iajs-3245	215	4	;	;	PUNCT
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iajs-3245	215	6	.	.	PROPN
iajs-3245	215	7	m.	m.	PROPN
iajs-3245	215	8	;	;	PUNCT
iajs-3245	215	9	suh	suh	PROPN
iajs-3245	215	10	,	,	PUNCT
iajs-3245	215	11	mw	mw	PROPN
iajs-3245	215	12	.	.	PROPN
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iajs-3245	215	16	bridges	bridge	NOUN
iajs-3245	215	17	using	use	VERB
iajs-3245	215	18	a	a	DET
iajs-3245	215	19	stochastic	stochastic	ADJ
iajs-3245	215	20	finite	finite	ADJ
iajs-3245	215	21	element	element	NOUN
iajs-3245	215	22	method	method	NOUN
iajs-3245	215	23	and	and	CCONJ
iajs-3245	215	24	surrogate	surrogate	ADJ
iajs-3245	215	25	models	model	NOUN
iajs-3245	215	26	.	.	PUNCT
iajs-3245	216	1	computers	computer	NOUN
iajs-3245	216	2	and	and	CCONJ
iajs-3245	216	3	structures	structure	NOUN
iajs-3245	216	4	2020	2020	NUM
iajs-3245	216	5	,	,	PUNCT
iajs-3245	216	6	238	238	NUM
iajs-3245	216	7	,	,	PUNCT
iajs-3245	216	8	106306	106306	NUM
iajs-3245	216	9	.	.	PUNCT
iajs-3245	217	1	https://doi.org/10.1016/j.compstruc.2020.106306	https://doi.org/10.1016/j.compstruc.2020.106306	PROPN
iajs-3245	217	2	21	21	NUM
iajs-3245	217	3	.	.	PUNCT
iajs-3245	218	1	wu	wu	PROPN
iajs-3245	218	2	,	,	PUNCT
iajs-3245	218	3	y.	y.	PROPN
iajs-3245	218	4	;	;	PUNCT
iajs-3245	218	5	zhang	zhang	PROPN
iajs-3245	218	6	,	,	PUNCT
iajs-3245	218	7	s.	s.	PROPN
iajs-3245	218	8	;	;	PUNCT
iajs-3245	218	9	cai	cai	X
iajs-3245	218	10	,	,	PUNCT
iajs-3245	218	11	cs	cs	PROPN
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iajs-3245	218	13	structural	structural	ADJ
iajs-3245	218	14	reliability	reliability	NOUN
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iajs-3245	218	16	of	of	ADP
iajs-3245	218	17	long	long	ADJ
iajs-3245	218	18	-	-	PUNCT
iajs-3245	218	19	span	span	NOUN
iajs-3245	218	20	bridges	bridge	NOUN
iajs-3245	218	21	under	under	ADP
iajs-3245	218	22	heavy	heavy	ADJ
iajs-3245	218	23	traffic	traffic	NOUN
iajs-3245	218	24	load	load	NOUN
iajs-3245	218	25	and	and	CCONJ
iajs-3245	218	26	strong	strong	ADJ
iajs-3245	218	27	wind	wind	NOUN
iajs-3245	218	28	.	.	PUNCT
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iajs-3245	219	2	of	of	ADP
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iajs-3245	219	5	2020	2020	NUM
iajs-3245	219	6	,	,	PUNCT
iajs-3245	219	7	146(9	146(9	NUM
iajs-3245	219	8	)	)	PUNCT
iajs-3245	219	9	,	,	PUNCT
iajs-3245	219	10	04020182	04020182	NUM
iajs-3245	219	11	.	.	PUNCT
iajs-3245	220	1	https://doi.org/10.1061/(asce)st.1943-541x.0002724	https://doi.org/10.1061/(asce)st.1943-541x.0002724	PROPN
iajs-3245	220	2	22	22	NUM
iajs-3245	220	3	.	.	PUNCT
iajs-3245	221	1	li	li	PROPN
iajs-3245	221	2	,	,	PUNCT
iajs-3245	221	3	s.	s.	PROPN
iajs-3245	221	4	;	;	PUNCT
iajs-3245	221	5	liang	liang	PROPN
iajs-3245	221	6	,	,	PUNCT
iajs-3245	221	7	x.	x.	PROPN
iajs-3245	221	8	;	;	PUNCT
iajs-3245	221	9	zhang	zhang	PROPN
iajs-3245	221	10	,	,	PUNCT
iajs-3245	221	11	z.	z.	PROPN
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iajs-3245	222	11	under	under	ADP
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iajs-3245	222	13	traffic	traffic	NOUN
iajs-3245	222	14	loads	load	NOUN
iajs-3245	222	15	.	.	PUNCT
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iajs-3245	223	2	in	in	ADP
iajs-3245	223	3	mechanical	mechanical	ADJ
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iajs-3245	223	6	,	,	PUNCT
iajs-3245	223	7	13(2	13(2	PROPN
iajs-3245	223	8	)	)	PUNCT
iajs-3245	223	9	,	,	PUNCT
iajs-3245	223	10	1687814021993540	1687814021993540	NUM
iajs-3245	223	11	.	.	PUNCT
iajs-3245	224	1	https://doi.org/10.1177/1687814021993540	https://doi.org/10.1177/1687814021993540	X
iajs-3245	225	1	23	23	NUM
iajs-3245	225	2	.	.	PUNCT
iajs-3245	226	1	zhang	zhang	PROPN
iajs-3245	226	2	,	,	PUNCT
iajs-3245	226	3	q.	q.	PROPN
iajs-3245	226	4	;	;	PUNCT
iajs-3245	226	5	liu	liu	PROPN
iajs-3245	226	6	,	,	PUNCT
iajs-3245	226	7	h.	h.	PROPN
iajs-3245	226	8	;	;	PUNCT
iajs-3245	226	9	xu	xu	PROPN
iajs-3245	226	10	,	,	PUNCT
iajs-3245	226	11	z.	z.	PROPN
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iajs-3245	226	15	maintenance	maintenance	NOUN
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iajs-3245	226	17	for	for	ADP
iajs-3245	226	18	deteriorating	deteriorate	VERB
iajs-3245	226	19	bridges	bridge	NOUN
iajs-3245	226	20	using	use	VERB
iajs-3245	226	21	a	a	DET
iajs-3245	226	22	multi	multi	ADJ
iajs-3245	226	23	-	-	ADJ
iajs-3245	226	24	objective	objective	ADJ
iajs-3245	226	25	approach	approach	NOUN
iajs-3245	226	26	.	.	PUNCT
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iajs-3245	227	4	,	,	PUNCT
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iajs-3245	227	6	,	,	PUNCT
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iajs-3245	227	8	.	.	PUNCT
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iajs-3245	228	2	24	24	NUM
iajs-3245	228	3	.	.	PUNCT
iajs-3245	229	1	yuen	yuen	PROPN
iajs-3245	229	2	,	,	PUNCT
iajs-3245	229	3	kk	kk	PROPN
iajs-3245	229	4	.	.	PROPN
iajs-3245	229	5	;	;	PUNCT
iajs-3245	229	6	wong	wong	PROPN
iajs-3245	229	7	,	,	PUNCT
iajs-3245	229	8	ss	ss	PROPN
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iajs-3245	229	10	;	;	PUNCT
iajs-3245	229	11	yeung	yeung	PROPN
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iajs-3245	230	7	,	,	PUNCT
iajs-3245	230	8	206	206	NUM
iajs-3245	230	9	,	,	PUNCT
iajs-3245	230	10	107279	107279	NUM
iajs-3245	230	11	.	.	PUNCT
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iajs-3245	231	2	https://doi.org/10.1016/j.jsv.2021.116595	https://doi.org/10.1016/j.jsv.2021.116595	PRON
iajs-3245	231	3	https://doi.org/10.1016/j.aap.2019.04.001	https://doi.org/10.1016/j.aap.2019.04.001	NOUN
iajs-3245	231	4	https://doi.org/10.1016/j.tranpol.2017.03.016	https://doi.org/10.1016/j.tranpol.2017.03.016	PROPN
iajs-3245	231	5	https://doi.org/10.1016/j.compbiomed.2018.04.002	https://doi.org/10.1016/j.compbiomed.2018.04.002	NOUN
iajs-3245	231	6	https://doi.org/10.1088/1742-6596/1879/3/032014	https://doi.org/10.1088/1742-6596/1879/3/032014	PROPN
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iajs-3245	231	8	https://doi.org/10.1016/j.engstruct.2020.111088	https://doi.org/10.1016/j.engstruct.2020.111088	PROPN
iajs-3245	231	9	https://doi.org/10.1016/j.strusafe.2018.12.003	https://doi.org/10.1016/j.strusafe.2018.12.003	PROPN
iajs-3245	231	10	https://ascelibrary.org/journal/jbenf2	https://ascelibrary.org/journal/jbenf2	ADP
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iajs-3245	231	13	https://doi.org/10.1016/j.jcsr.2021.106618	https://doi.org/10.1016/j.jcsr.2021.106618	PROPN
iajs-3245	231	14	https://doi.org/10.1016/j.compstruc.2020.106306	https://doi.org/10.1016/j.compstruc.2020.106306	VERB
iajs-3245	231	15	https://doi.org/10.1061/(asce)st.1943-541x.0002724	https://doi.org/10.1061/(asce)st.1943-541x.0002724	PROPN
iajs-3245	231	16	https://doi.org/10.1177/1687814021993540	https://doi.org/10.1177/1687814021993540	X
iajs-3245	231	17	https://doi.org/10.1016/j.autcon.2020.103258	https://doi.org/10.1016/j.autcon.2020.103258	ADP
iajs-3245	231	18	https://doi.org/10.1016/j.ress.2020.107279	https://doi.org/10.1016/j.ress.2020.107279	PROPN
iajs-3245	231	19	1	1	NUM
iajs-3245	231	20	.	.	PUNCT
iajs-3245	231	21	introduction	introduction	NOUN
iajs-3245	231	22	2	2	NUM
iajs-3245	231	23	.	.	PUNCT
iajs-3245	231	24	multivariate	multivariate	NOUN
iajs-3245	231	25	bayesian	bayesian	NOUN
iajs-3245	231	26	binary	binary	PROPN
iajs-3245	231	27	logistic	logistic	PROPN
iajs-3245	231	28	regression	regression	NOUN
iajs-3245	231	29	model	model	NOUN
iajs-3245	231	30	3	3	NUM
iajs-3245	231	31	.	.	PUNCT
iajs-3245	232	1	bayesian	bayesian	NOUN
iajs-3245	232	2	formulation	formulation	NOUN
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iajs-3245	232	4	markov	markov	NOUN
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iajs-3245	232	9	.	.	PUNCT
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iajs-3245	233	8	heart	heart	NOUN
iajs-3245	233	9	disease	disease	NOUN
iajs-3245	233	10	dataset	dataset	VERB
iajs-3245	233	11	analysis	analysis	NOUN
iajs-3245	233	12	6	6	NUM
iajs-3245	233	13	.	.	PUNCT
iajs-3245	233	14	results	result	NOUN
iajs-3245	233	15	and	and	CCONJ
iajs-3245	233	16	discussion	discussion	NOUN
iajs-3245	233	17	7	7	NUM
iajs-3245	233	18	.	.	PUNCT
iajs-3245	234	1	conclusion	conclusion	NOUN
iajs-3245	234	2	references	reference	NOUN