An implementation of a method for computing the uncertainty in inferred probabilities in belief networks

Authors:
Peter Che;Richard E. Neapolitan;James Kenevan;Martha Evens
Affiliations:
Computer Science Department, Illinois Institute of Technology, Chicago, IL;Computer Science Department, Northeastern Illinois University, Chicago, IL;Computer Science Department, Illinois Institute of Technology, Chicago, IL;Computer Science Department, Illinois Institute of Technology, Chicago, IL
Venue:
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Year:
1993

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Abstract

In recent years the belief network has been used increasingly to model systems in AI that must perform uncertain inference. The development of efficient algorithms for probabilistic inference in belief networks has been a focus of much research in AI. Efficient algorithms for certain classes of belief networks have been developed, but the problem of reporting the uncertainty in inferred probabilities has received little attention. A system should not only be capable of reporting the values of inferred probabilities and/or the favorable choices of a decision; it should report the range of possible error in the inferred probabilities and/or choices. Two methods have been developed and implemented for determining the variance in inferred probabilities in belief networks. These methods, the Approximate Propagation Method and the Monte Carlo Integration Method are discussed and compared in this paper.