Learning Bayesian Nets that Perform Well

2013/02/06 by Russell Greiner, Greiner, Russell, Adam J. Grove +3
Computer Science · Decision Sciences · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #Data Quality and Management #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Algorithms #cs.AI #cs.LG

paper · pdf · doi:10.48550/arxiv.1302.1542

Appears in Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI1997)

arxiv created 2013/02/06 · openalex publication_date 2013/02/06 · arxiv updated 2013/02/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

A Bayesian net (BN) is more than a succinct way to encode a probabilistic distribution; it also corresponds to a function used to answer queries. A BN can therefore be evaluated by the accuracy of the answers it returns. Many algorithms for learning BNs, however, attempt to optimize another criterion (usually likelihood, possibly augmented with a regularizing term), which is independent of the distribution of queries that are posed. This paper takes the "performance criteria" seriously, and considers the challenge of computing the BN whose performance - read "accuracy over the distribution of queries" - is optimal. We show that many aspects of this learning task are more difficult than the corresponding subtasks in the standard model.

Citations

• On the Sample Complexity of Learning Bayesian Networks
• Approximating probabilistic inference in Bayesian belief networks is NP-hard

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