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Erschienen in:
1994 | OriginalPaper | Buchkapitel
Markov Chain Monte Carlo Methods for Hierarchical Bayesian Expert Systems
verfasst von : Jeremy C. York, David Madigan
Erschienen in: Selecting Models from Data
Verlag: Springer New York
Enthalten in: Professional Book Archive
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In a hierarchical Bayesian expert system, the probabilities relating the variables are not known precisely; rather, imprecise knowledge of these probabilities is described by placing prior distributions on them. After obtaining data, one would like to update those distributions to reflect the new information gained; however, this can prove difficult computationally if the observed data are incomplete. This paper describes a way around these difficulties—use of Markov chain Monte Carlo methods.