PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models
Joris Mooij
Journal of Machine Learning Research Volume 11, pp. 2169-2173, 2010. ISSN ISSN 1532-4435

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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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:7861
Deposited By:Joris Mooij
Deposited On:17 March 2011