Confidence inference in bayesian networks

Authors:
Jian Cheng;Marek J. Druzdzel
Affiliations:
Decision Systems Laboratory, School of Information Sciences and Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA;ReasonEdge Technologies, Pte, Ltd, Singapore, Republic of Singapore and Decision Systems Laboratory, School of Information Sciences and Intelligent Systems Program, University of Pittsburgh, Pitts ...
Venue:
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Year:
2001

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Abstract

We present two sampling algorithms for probabilistic confidence inference in Bayesian networks. These two algorithms (we call them AIS-BN-µ and AIS-BN-σ algorithms) guarantee that estimates of posterior probabilities are with a given probability within a desired precision bound. Our algorithms are based on recent advances in sampling algorithms for (1) estimating the mean of bounded random variables and (2) adaptive importance sampling in Bayesian networks. In addition to a simple stopping rule for sampling that they provide, the AIS-BN-µ and AIS-BN-σ algorithms are capable of guiding the learning process in the AIS-BN algorithm. An empirical evaluation of the proposed algorithms shows excellent performance, even for very unlikely evidence.