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, 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 0.3.0 (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.

BibTeX Entry: Download

Supported Operating Systems: Cygwin, Linux, Mac Os X

Data Formats: Ascii

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

Archive: download here

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