Bayesian networks on dirichlet distributed vectors

Wray Buntine*, Lan Du, Petteri Nurmi

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    2 Citations (Scopus)

    Abstract

    Exact Bayesian network inference exists for Gaussian and multinomial distributions. For other kinds of distributions, approximations or restrictions on the kind of inference done are needed. In this paper we present generalized networks of Dirichlet distributions, and show how, using the two-parameter Poisson-Dirichlet distribution and Gibbs sampling, one can do approximate inference over them. This involves integrating out the probability vectors but leaving auxiliary discrete count vectors in their place. We illustrate the technique by extending standard topic models to "structured" documents, where the document structure is given by a Bayesian network of Dirichlets.

    Original languageEnglish
    Title of host publicationProceedings of the 5th European Workshop on Probabilistic Graphical Models, PGM 2010
    Pages33-40
    Number of pages8
    Publication statusPublished - 2010
    Event5th European Workshop on Probabilistic Graphical Models, PGM 2010 - Helsinki, Finland
    Duration: 13 Sept 201015 Sept 2010

    Publication series

    NameProceedings of the 5th European Workshop on Probabilistic Graphical Models, PGM 2010

    Conference

    Conference5th European Workshop on Probabilistic Graphical Models, PGM 2010
    Country/TerritoryFinland
    CityHelsinki
    Period13/09/1015/09/10

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