Project details for Libra

Logo Libra 1.1.2d

by lowd - February 4, 2016, 08:51:50 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

view (12 today), download ( 8 today ), 0 subscriptions


The Libra Toolkit is a collection of algorithms for learning and inference with discrete probabilistic models, including Bayesian networks, Markov networks, dependency networks, sum-product networks, arithmetic circuits, and mixtures of trees.

Learning algorithms -- Structure learning for BNs, MNs, DNs, SPNs, and mixtures of trees; learning tractable BNs and MNs with ACs; MN weight learning

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

Libra's strengths include structure learning, tractable models, and exploiting sparse factors.

Changes to previous version:

Version 1.1.2d (12/29/2015):

  • Minor fixes to scripts
  • Published in JMLR ML-OSS!
BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
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, Sum Product Networks
Archive: download here

Other available revisons

Version Changelog Date

Version 1.1.2d (12/29/2015):

  • Minor fixes to scripts
  • Published in JMLR ML-OSS!
February 4, 2016, 08:51:50

Version 1.1.2c (6/24/2015):

  • Libra can now be installed via OPAM as well. To install OPAM, see: . Then run: "opam install libra-tk".
  • Updated documentation to describe OPAM installation.
June 25, 2015, 00:10:25

Version 1.1.2 (6/10/2015):

  • Switched to 2-clause BSD license; added license headers to all source files.
  • Switched to OASIS build system for much cleaner compilation and installation procedures.
June 11, 2015, 08:07:21

Version 1.1.1 (5/21/2015):

  • Many minor fixes to documentation, scripts, and code.
May 22, 2015, 10:16:52

Version 1.1.0 (3/26/2014):

  • Added SPN library
  • Added API documentation for all libraries
  • Introduced libra script as a unified interface to all programs
  • Many minor improvements to the code, interface, and documentation.
March 27, 2015, 06:29:45

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

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

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

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

Initial Announcement on

April 24, 2010, 11:38:24


No one has posted any comments yet. Perhaps you'd like to be the first?

Leave a comment

You must be logged in to post comments.