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  • Last update: 6 years ago
  • Total downloads: 100
  • Operating system: Mac OS X
  • Publisher: libDAI Team
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mac default iconlibDAI For Mac Publisher's description

libDAI is a free/open source C++ library

libDAI is a free/open source C++ library (licensed under GPL 2+) 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.

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
* (limited) MatLab interface
* (experimental) python interface
* (experimental) 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 was recently presented at MLOSS 08; the slides are available, or you can watch the talk at videolectures.net.

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 [KFL01];
* Fractional Belief Propagation [WiH03];
* Tree-Reweighted Belief Propagation [WJW03];
* Tree Expectation Propagation [MiQ04];
* Generalized Belief Propagation [YFW05];
* Double-loop GBP [HAK03];
* Various variants of Loop Corrected Belief Propagation [MoK07, MoR05];
* Gibbs sampler;
* Conditioned Belief Propagation [EaG09];
* Decimation algorithm.

These inference methods can be used to calculate partition sums, marginals over subsets of variables, and MAP states (the joint state of variables that has maximum probability).

In addition, libDAI supports parameter learning of conditional probability tables by Expectation Maximization.

What's New in This Release:

В· Removed interfaces deprecated in 0.2.6.
Fixed a bug in JTree::findMaximum() (reported by zhengyun84 and Dhruv Batra):
В· if one or more variables had a MAP belief with more than one maximum, an
В· incorrect MAP state could result.

System Requirements:

No special requirements.
Program Release Status: New Release
Program Install Support: Install and Uninstall

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