About: The glm-ie toolbox contains scalable estimation routines for GLMs (generalised linear models) and SLMs (sparse linear models) as well as an implementation of a scalable convex variational Bayesian inference relaxation. We designed the glm-ie package to be simple, generic and easily expansible. Most of the code is written in Matlab including some MEX files. The code is fully compatible to both Matlab 7.x and GNU Octave 3.2.x. Probabilistic classification, sparse linear modelling and logistic regression are covered in a common algorithmical framework allowing for both MAP estimation and approximate Bayesian inference.

New matrix class Bugfixes More examples New penalty and potential functions Group sparsity

About: libDAI provides free & open source implementations of various (approximate) inference methods for graphical models with discrete variables, including Bayesian networks and Markov Random Fields.

libDAI release 0.2.7 is a bug-fix release which fixes a bug in the junction-tree MAP inference which could yield incorrect results in some cases. This release will accompany a forthcoming JMLR publication about libDAI.

About: The Libra machine learning toolkit includes implementations of a variety of algorithms for learning and inference with Bayesian networks, Markov networks, and arithmetic circuits. Libra's strength is exploiting context-specific independence to allow exact inference in models with high treewidth.

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.

About: A C++ toolkit containing machine learning algorithms and tools that facilitate creating complex software in C++ to solve real world problems.

Minor bug fixes

About: The GPML toolbox is a flexible and generic Octave 3.2.x and Matlab 7.x implementation of inference and prediction in Gaussian Process (GP) models.

Initial Announcement on mloss.org.

About: stroll (STRuctured Output Learning Library) is a library for Structured Output Learning.

Initial Announcement on mloss.org.