Project details for Libra

Logo Libra 0.1.0

by lowd - April 24, 2010, 11:38:24 CET [ Project Homepage BibTeX Download ]

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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:

Initial Announcement on mloss.org.

BibTeX Entry: Download
URL: Project Homepage
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

Other available revisons

Version Changelog Date
1.0.1

Version 1.0.1 (3/30/2014):

  • Several new algorithms -- acmn, learning ACs using MNs; idspn, SPN structure learning; mtlearn, learning mixtures of trees
  • Several new support programs -- spquery, for exact inference in SPNs; spn2ac, for converting SPNs to ACs
  • Renamed aclearnstruct to acbn
  • Replaced aclearnstruct -noac with separate bnlearn program
  • ...and many more small changes and fixes, throughout!
March 30, 2014, 09:42:00
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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