Surrogate Model–Assisted Learning of Conditional Probability Tables of Bayesian Networks Describing Structural Systems

dc.contributor.author	Mun, Changuk
dc.contributor.author	Song, Junho
dc.date.accessioned	2026-08-20T21:29:02Z
dc.date.available	2026-08-20T21:29:02Z
dc.date.copyright	2025/12/31
dc.date.issued	2026
dc.identifier.other	AJRUA6.RUENG-1799.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl1/handle/yetl/4314531
dc.description.abstract	AbstractBayesian network (BN) is an effective tool to describe and interpret the probability distribution of a complex phenomenon. Essential building blocks in discrete BNs are conditional probability tables (CPTs) quantifying statistical dependence ...
dc.publisher	American Society of Civil Engineers
dc.title	Surrogate Model–Assisted Learning of Conditional Probability Tables of Bayesian Networks Describing Structural Systems
dc.type	Journal Article
dc.journal.volume	12
dc.journal.issue	1
dc.journal.title	ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
dc.identifier.doi	10.1061/AJRUA6.RUENG-1799
dc.journal.fristpage	04025113-1
dc.journal.lastpage	04025113-14
dc.page	14
dc.tree	ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2026:;Volume ( 012 ):;issue: 001
dc.contenttype	Fulltext