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  Bounds on marginal probability distributions

Mooij, J. (2009). Bounds on marginal probability distributions. Advances in neural information processing systems 21: 22nd Annual Conference on Neural Information Processing Systems 2008, 1105-1112.

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 Creators:
Mooij, JM1, Author           
Koller, Editor
D., Editor
Schuurmans, D., Editor
Bengio, Y., Editor
Bottou, L., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Abstract: We propose a novel bound on single-variable marginal probability distributions in factor graphs with discrete variables. The bound is obtained by propagating local bounds (convex sets of probability distributions) over a subtree of the factor graph, rooted in the variable of interest. By construction, the method not only bounds the exact marginal probability distribution of a variable, but also its approximate Belief Propagation marginal ("belief"). Thus, apart from providing a practical means to calculate bounds on marginals, our contribution also lies in providing a better understanding of the error made by Belief Propagation. We show that our bound outperforms the state-of-the-art on some inference problems arising in medical diagnosis.

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 Dates: 2009-06
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: ISBN: 978-1-605-60949-2
URI: http://nips.cc/Conferences/2008/
BibTex Citekey: 5407
 Degree: -

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Title: Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS 2008)
Place of Event: Vancouver, BC, Canada
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Title: Advances in neural information processing systems 21 : 22nd Annual Conference on Neural Information Processing Systems 2008
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Red Hook, NY, USA : Curran
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1105 - 1112 Identifier: -