Description:

This is a Matlab/C++ "toolbox" of code for learning and inference with graphical models. It is focused on parameter learning using marginalization in the high-treewidth setting. Though the code is, in principle, domain independent, I've developed it with vision problems in mind, particularly for learning Conditional Random Fields (CRFs). This means that the code is A) efficient (all the inference algorithms are implemented in C++) and B) can handle arbitrary graph structures.

There are, at present, a bunch of limitations:

All the inference algorithms are for marginal inference. No MAP inference, at all.
The code handles pairwise graphs only
All variables must have the same number of possible values.
For tree-reweighted belief propagation, a single edge appearance probability must be used for all edges

A draft paper describing all the algorithms is available here.

Changes to previous version:

Initial Announcement on mloss.org.

BibTeX Entry: Download

Supported Operating Systems: Linux, Mac Os X

Data Formats: Agnostic

Tags: Crf, Graphical Models, Markov Random Fields

Archive: download here

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