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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. Changes: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.
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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. Changes:Version 0.3.0 (8/01/2010):
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About: The library is focused on implementation of propagation based approximate inference methods. Also implemented are a clique tree based exact inference, Gibbs sampling, and the mean field algorithm. Changes:Initial Announcement on mloss.org.
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About: JProGraM (PRObabilistic GRAphical Models in Java) is a statistical machine learning library. It supports learning and inference for various probabilistic graphical models, along with parametric, semiparametric, and nonparametric density estimation. Changes:JProGraM 10.5 -- CHANGE LOG Release date: May 30, 2010 -- Support for continuous graphical models has been added: Gaussian, nonparanormal, and kernel-based Markov random fields, hybrid random fields and Bayes nets are now implemented; -- Routines for kernel-based conditional density estimation (Nadaraya-Watson estimators) have been implemented, with support for scalable dual-tree recursion techniques (used in the bandwidth selection routines); -- Methods for generating arbitrarily shaped multivariate density functions and for sampling datasets from them have been added; -- Independent Component Analysis is now also supported (by wrapping the FastICA library); -- Gaussian mixture models have been greatly improved by adding support for expectation-maximization, fixing some numerical stability issues and differentiating a simpler version with diagonal covariance matrices from a more complex version with full covariance matrices; -- Other minor features have been added, and a number of corrections have been introduced. Note that the Gaussian and nonparanormal Markov random fields relying on the graphical lasso technique require a working R distribution (http://www.r-project.org/) to be installed on your system, including in particular the external glasso package (http://www-stat.stanford.edu/~tibs/glasso/). Provided that R and the glasso package are correctly installed, the relevant JProGraM routines are able to exploit the R installation without any manual intervention. This means that users of the JProGraM library can sinmply call the routines executing the graphical lasso within their Java code without the need to manipulate any R code. (This has been tested successfully on several Linux distributions, but not on Windows or Mac OS X). JProGraM 9.1 -- CHANGE LOG Release date: January 29, 2009 -- Principal Components Analysis is now supported; -- A number of bugs within the ninofreno.gmm and ninofreno.clustering packages have been fixed; -- Other minor features have been added (especially within the MyMath class). JProGraM 8.10 -- CHANGE LOG Release date: October 7, 2008 The following algorithms are now supported by JProGraM: -- K-Means (for clustering); -- Kaufman-Rousseuw algorithm for initializing cluster centroids; -- Gaussian Mixture Model for probability density function estimation. JProGraM 8.6 -- CHANGE LOG Release date: June 8, 2008 The following statistical models are now supported by JProGraM: -- Parzen Windows for probability density function estimation; -- Probabilistic decision trees for discrete pattern classification; -- Dependency networks for discrete pseudo-likelihood estimation. JProGraM 8.1 Release date: February 16, 2008 The following statistical models are supported by JProGraM: -- Bayesian networks; -- Markov random fields; -- Hybrid random fields.
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About: This toolbox provides functions for maximizing and minimizing submodular set functions, with applications to Bayesian experimental design, inference in Markov Random Fields, clustering and others. Changes:
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About: A Java framework for statistical analysis and classification of biological sequences Changes:March 2, 2010: Jstacs 1.3.1 released
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About: GMRFLib is a library in C for fast and exact simulation of Gaussian Markov Random Fields (GMRF) on graphs.unconditional simulation of a GMRF, conditional simulation from a GMRF Changes:Initial Announcement on mloss.org.
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