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  Bounds on marginal probability distributions

Mooij, J., & Kappen, B. (2009). Bounds on marginal probability distributions. In D. Koller, D. Schuurmans, Y. Bengio, & L. Bottou (Eds.), Advances in neural information processing systems 21 (pp. 1105-1112). Red Hook, NY, USA: Curran.

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 Creators:
Mooij, JM1, 2, Author           
Kappen, B, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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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: -
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 Rev. Type: -
 Identifiers: 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
Start-/End Date: 2008-12-08 - 2008-12-10

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Title: Advances in neural information processing systems 21
Source Genre: Proceedings
 Creator(s):
Koller, D, Editor
Schuurmans, D, Editor
Bengio, Y, Editor
Bottou, L, Editor
Affiliations:
-
Publ. Info: Red Hook, NY, USA : Curran
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1105 - 1112 Identifier: ISBN: 978-1-60560-949-2