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Learning Conditional Probability Distributions for Robust Probabilistic Inference in Bayesian Network

Xinran Wu,Kun Yue,Huashuai Liu,Liang Duan

Published 2025 in International Conference on Information and Knowledge Management

ABSTRACT

Bayesian Network (BN) has been widely employed for many applications like medical diagnosis due to its ability to deal with probabilistic inferences. Real-world inference tasks in BN cannot be robustly processed by classic search-based inference algorithms, since the conditional probabilities w.r.t. given arbitrary evidence values may be missing (i.e., not included) in the conditional probability tables (CPTs). Most of the existing methods, relying on imputation models, density estimation models or deep neural networks, cannot accurately learn these missing probabilities. To this end, we incorporate the idea of learning and search for robust probabilistic inferences in BN. Firstly, we decompose the probabilistic inference task into missing and existing probability factors, ensuring the consistency of their probability spaces. Secondly, we define the Wasserstein distance between missing and existing probability factors, and incorporate the idea of generative adversarial network to obtain missing probability factors with the minimal Wasserstein distance. Finally, we give the algorithm for robust probabilistic inferences with arbitrary evidence values, which could also be used to deal with the probabilistic inferences with arbitrary query values. Extensive experiments on synthetic and real-world datasets are conducted to demonstrate the superiority of our proposed method.

PUBLICATION RECORD

  • Publication year

    2025

  • Venue

    International Conference on Information and Knowledge Management

  • Publication date

    2025-11-10

  • Fields of study

    Medicine, Computer Science

  • Identifiers
    DOI 10.1145/3746252.3761154
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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