Uncertainty in Artificial Intelligence
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HUGS: Combining Exact Inference and Gibbs Sampling in Junction Trees
Uffe Kjærulff
Abstract:
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.
Keywords: Junction tree, propagation, Monte-Carlo simulation, Gibbs sampling.
Pages: 368-375
PS Link:
PDF Link: /papers/95/p368-kjaerulff.pdf
BibTex:
@INPROCEEDINGS{Kjærulff95,
AUTHOR = "Uffe Kjærulff ",
TITLE = "HUGS: Combining Exact Inference and Gibbs Sampling in Junction Trees",
BOOKTITLE = "Proceedings of the Eleventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-95)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1995",
PAGES = "368--375"
}


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