Learning Bayesian Nets that Perform Well

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.

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

References in corpus (1)

• On the Sample Complexity of Learning Bayesian Networks

Cited by in corpus (6)

• Comparing Bayesian Network Classifiers
• Exploiting Qualitative Knowledge in the Learning of Conditional Probabilities of Bayesian Networks
• Bayesian Error-Bars for Belief Net Inference
• On the Number of Samples Needed to Learn the Correct Structure of a Bayesian Network
• When do Numbers Really Matter?
• Estimating Well-Performing Bayesian Networks using Bernoulli Mixtures