Description:

The Libra machine learning toolkit includes implementations of a variety of algorithms for learning and inference with Bayesian networks, Markov networks, and arithmetic circuits:

Learning algorithms -- Structure learning for BNs, ACs, and dependency networks; Chow-Liu algorithm; AC weight learning

Inference algorithms -- Mean field, belief propagation, max-product, Gibbs sampling, AC variable elimination, AC exact inference

Libra's strength is exploiting context-specific independence (such as decision tree CPDs) to allow exact inference in models with high treewidth.

Changes to previous version:

Version 0.4.0 (7/06/2011): * MF inference in DNs (mf -depnet) * Max-product algorithm for BNs and MNs (maxprod) * MPE inference in ACs (acquery -mpe) * Added support for UAI MN file format. * New fstats utility to get basic file statistics for most file types supported by Libra * And more!

BibTeX Entry: Download

URL: Project Homepage

Supported Operating Systems: Cygwin, Linux, Mac Os X

Data Formats: Ascii

Tags: Structure Learning, Approximate Inference, Bayesian Networks, Markov Random Fields, Dependency Networks, Arithmetic Circuits, Exact Inference, Markov Networks

Archive: download here

Other available revisons

Version	Changelog	Date
0.4.0	Version 0.4.0 (7/06/2011): * MF inference in DNs (mf -depnet) * Max-product algorithm for BNs and MNs (maxprod) * MPE inference in ACs (acquery -mpe) * Added support for UAI MN file format. * New fstats utility to get basic file statistics for most file types supported by Libra * And more!	July 6, 2011, 09:40:25
0.3.0	Version 0.3.0 (8/01/2010): New data structure and functions for Markov networks with factors that are trees, tables, conjunctive features, or sets of conjunctive features. Added MN support to ACVE, BP, MF, Gibbs, and more. AC, BN, and MN scoring is now handled by a single program, mscore. Added mscore utility to convert between .xmod and .bif formats, or to go from .xmod/.bif to .mn (Markov network format). Added -noac option to aclearnstruct, so that it can be used to learn a Bayesian network that isn't represented as a circuit. Added dependency network learner (dnlearn) Extended tutorial, revised manual, and added more tests.	August 2, 2010, 07:21:28
0.2.0	Version 0.2.0 (6/08/2010): BP now supports table CPDs, not just trees Gibbs sampling now supports dependency networks with -depnet flag (experimental). Added -norb flag to disable Rao-Blackwellization in Gibbs sampling Fixed expat compilation under OS X Greatly expanded user manual Tweaks to the output of inference algorithms Added more automated tests, based on the tutorial	June 9, 2010, 00:43:28
0.1.0	Initial Announcement on mloss.org.	April 24, 2010, 11:38:24

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