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BAYESIAN SEQUENTIAL LEARNING FROM INCOMPLETE DATA ON DECOMPOSABLE GRAPHICAL MODELS

  • KURODA Masahiro
    Department of Computer Science and Mathematics, Kurashiki University of Science and the Arts
  • GENG Zhi
    Department of Probability and Statistics, Peking University
  • 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.

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Details

  • CRID
    1571417126884046848
  • NII Article ID
    110001235638
  • NII Book ID
    AA10823693
  • ISSN
    09152350
  • Text Lang
    en
  • Data Source
    • CiNii Articles

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