libDAI is a free and open source C++ development library 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.
libDAI is targeted at researchers. To be able to use libDAI, a good understanding of graphical models is needed.
The best way to use libDAI is by writing C++ code that invokes the library; in addition, part of the functionality is accessibly by using the:
· command line interface
· MatLab interface
· python interface
· octave interface.
libDAI can be used to implement novel (approximate) inference algorithms and to easily compare the accuracy and performance with existing algorithms that have been implemented already.
libDAI is a cross-platform library which works under Mac OS X, Linux and Windows.
Here are some key features of "libDAI":
· Exact inference by brute force enumeration;
· Exact inference by junction-tree methods;
· Mean Field;
· Loopy Belief Propagation
· Fractional Belief Propagation
· Tree-Reweighted Belief Propagation
· Tree Expectation Propagation
· Generalized Belief Propagation
· Double-loop GBP
· Various variants of Loop Corrected Belief Propagation
· Gibbs sampler
· Conditioned Belief Propagation
What`s New in This Release: [ read full changelog ]
· Removed interfaces deprecated in 0.2.6.
Fixed a bug in JTree::findMaximum() (reported by zhengyun84 and Dhruv Batra):
· if one or more variables had a MAP belief with more than one maximum, an
· incorrect MAP state could result.
