Abstract
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.
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© 1994 Springer-Verlag New York
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York, J.C., Madigan, D. (1994). Markov Chain Monte Carlo Methods for Hierarchical Bayesian Expert Systems. In: Cheeseman, P., Oldford, R.W. (eds) Selecting Models from Data. Lecture Notes in Statistics, vol 89. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2660-4_45
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DOI: https://doi.org/10.1007/978-1-4612-2660-4_45
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