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  libDAI

Mooij, J. (2008). libDAI. Talk presented at NIPS 2008 Workshop: Machine Learning Open-Source Software. Whistler, BC, Canada. 2008-12-12.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0003-A08C-A Version Permalink: http://hdl.handle.net/21.11116/0000-0003-A0A2-0
Genre: Talk

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https://mloss.org/workshop/nips08/ (Table of contents)
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Mooij, JM1, 2, 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: libDAI is a free and open source C++ library (licensed under GPL) that provides implementations of various (approximate) inference methods for discrete graphical models. libDAI supports arbitrary factor graphs with discrete variables; this includes discrete Markov Random Fields and Bayesian Networks. The library is targeted at researchers; to be able to use the library, a good understanding of graphical models is needed. Currently, libDAI supports the following (approximate) inference methods: exact inference by brute force enumeration, exact inference by junction-tree methods, Mean Field, Loopy Belief Propagation, Tree Expectation Propagation, Generalized Belief Propagation, Double-loop GBP, and various variants of Loop Corrected Belief Propagation. Planned extensions are Gibbs sampling and IJGP, as well as various methods for obtaining bounds on the partition sum and on marginals (Bound Propagation, Box Propagation, Tree-based Reparameterization).

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 Dates: 2008-12
 Publication Status: Published online
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Title: NIPS 2008 Workshop: Machine Learning Open-Source Software
Place of Event: Whistler, BC, Canada
Start-/End Date: 2008-12-12
Invited: Yes

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Title: NIPS 2008: Mini Symposia & Workshops
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 70 Identifier: -