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  libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models

Mooij, J. (2010). libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models. The Journal of Machine Learning Research, 11, 2169-2173.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BEAC-1 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-6AE1-9
Genre: Journal Article

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Mooij, JM1, 2, Author              
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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: This paper describes the software package libDAI, a free open source C++ library that provides implementations of various exact and approximate inference methods for graphical models with discrete-valued variables. libDAI supports directed graphical models (Bayesian networks) as well as undirected ones (Markov random fields and factor graphs). It offers various approximations of the partition sum, marginal probability distributions and maximum probability states. Parameter learning is also supported. A feature comparison with other open source software packages for approximate inference is given. libDAI is licensed under the GPL v2+ license and is available at http://www.libdai.org.

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 Dates: 2010-08
 Publication Status: Published in print
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 Identifiers: BibTex Citekey: 6762
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Title: The Journal of Machine Learning Research
Source Genre: Journal
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Publ. Info: Cambridge, MA : MIT Press
Pages: - Volume / Issue: 11 Sequence Number: - Start / End Page: 2169 - 2173 Identifier: ISSN: 1532-4435
CoNE: https://pure.mpg.de/cone/journals/resource/111002212682020_1