Description:

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

Learning algorithms -- Structure learning for BNs and ACs; 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 0.2.0 (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

BibTeX Entry: Download

Supported Operating Systems: Cygwin, Linux, Mac Os X

Data Formats: Ascii

Tags: Structure Learning, Approximate Inference, Bayesian Networks, Icml2010, Arithmetic Circuits, Exact Inference

Archive: download here

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