An Implementation of a Method for Computing the Uncertainty in Inferred Probabilities in Belief Networks
Peter Che, Richard Neapolitan, James Kenevan, Martha Evens
In recent years the belief network has been used increasingly to model systems in Al 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.
PDF Link: /papers/93/p292-che.pdf
AUTHOR = "Peter Che
and Richard Neapolitan and James Kenevan and Martha Evens",
TITLE = "An Implementation of a Method for Computing the Uncertainty in Inferred Probabilities in Belief Networks",
BOOKTITLE = "Proceedings of the Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-93)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1993",
PAGES = "292--300"