BAYESIAN SEQUENTIAL LEARNING FROM INCOMPLETE DATA ON DECOMPOSABLE GRAPHICAL MODELS
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- KURODA Masahiro
- Department of Computer Science and Mathematics, Kurashiki University of Science and the Arts
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- GENG Zhi
- Department of Probability and Statistics, Peking University
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- NIKI Naoto
- Department of Management Science, Science University of Tokyo
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Abstract
In this paper, we discuss the Bayesian sequential learning on probabilities from incomplete data in decomposable graphical models. We give exact formulas of the posterior distribution, and the posterior mean and the posterior second moment based on a hyper Dirichlet prior distribution and an incomplete observation. The posterior distribution is usually a mixture hyper Dirichlet distribution when there exist incomplete data. In order to approximate the mixture posterior, we choose a single hyper Dirichlet distribution which has the same mean and the same average variance sum as those of the exact posterior.
Journal
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- Journal of the Japanese Society of Computational Statistics
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Journal of the Japanese Society of Computational Statistics 14 (1), 11-29, 2001-12-01
Japanese Society of Computational Statistics
