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

    Source: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2026:;Volume ( 012 ):;issue: 001::page 04025113-1
    Author:
    Mun, Changuk
    ,
    Song, Junho
    DOI: 10.1061/AJRUA6.RUENG-1799
    Publisher: American Society of Civil Engineers
    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 ...
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      Surrogate Model–Assisted Learning of Conditional Probability Tables of Bayesian Networks Describing Structural Systems

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    https://yetl.yabesh.ir/yetl1/handle/yetl/4314531
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    • ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering

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    contributor authorMun, Changuk
    contributor authorSong, Junho
    date accessioned2026-08-20T21:29:02Z
    date available2026-08-20T21:29:02Z
    date copyright2025/12/31
    date issued2026
    identifier otherAJRUA6.RUENG-1799.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314531
    description abstractAbstractBayesian 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 ...
    publisherAmerican Society of Civil Engineers
    titleSurrogate Model–Assisted Learning of Conditional Probability Tables of Bayesian Networks Describing Structural Systems
    typeJournal Article
    journal volume12
    journal issue1
    journal titleASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
    identifier doi10.1061/AJRUA6.RUENG-1799
    journal fristpage04025113-1
    journal lastpage04025113-14
    page14
    treeASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2026:;Volume ( 012 ):;issue: 001
    contenttypeFulltext
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