Help Privacy Policy Disclaimer
  Advanced SearchBrowse







Mooij,  JM
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available

Mooij, J. (2008). libDAI. Talk presented at NIPS 2008 Workshop: Machine Learning Open-Source Software. Whistler, BC, Canada. 2008-12-12.

Cite as: https://hdl.handle.net/21.11116/0000-0003-A08C-A
libDAI is a free and open source C++ library (licensed under GPL) that provides implementations of
various (approximate) inference methods for discrete graphical models. libDAI supports arbitrary factor
graphs with discrete variables; this includes discrete Markov Random Fields and Bayesian Networks. The
library is targeted at researchers; to be able to use the library, a good understanding of graphical models
is needed. Currently, libDAI supports the following (approximate) inference methods: exact inference by
brute force enumeration, exact inference by junction-tree methods, Mean Field, Loopy Belief Propagation,
Tree Expectation Propagation, Generalized Belief Propagation, Double-loop GBP, and various variants of
Loop Corrected Belief Propagation. Planned extensions are Gibbs sampling and IJGP, as well as various
methods for obtaining bounds on the partition sum and on marginals (Bound Propagation, Box Propagation,
Tree-based Reparameterization).