HUGS: Combining Exact Inference and Gibbs Sampling in Junction Trees

Dawid, Kjaerulff and Lauritzen (1994) provided a preliminary description of a hybrid between Monte-Carlo sampling methods and exact local computations in junction trees. Utilizing the strengths of both methods, such hybrid inference methods has the potential of expanding the class of problems which...

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Bibliographic Details
Published in:arXiv.org 2013-02
Main Author: Kjærulff, Uffe
Format: Article
Language:English
Subjects:
Bayesian analysis
Economic models
Inference
Message passing
Monte Carlo simulation
Sampling methods
Trees
Online Access:Get full text
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Summary:Dawid, Kjaerulff and Lauritzen (1994) provided a preliminary description of a hybrid between Monte-Carlo sampling methods and exact local computations in junction trees. Utilizing the strengths of both methods, such hybrid inference methods has the potential of expanding the class of problems which can be solved under bounded resources as well as solving problems which otherwise resist exact solutions. The paper provides a detailed description of a particular instance of such a hybrid scheme; namely, combination of exact inference and Gibbs sampling in discrete Bayesian networks. We argue that this combination calls for an extension of the usual message passing scheme of ordinary junction trees.
ISSN:2331-8422