Description:

JProGraM is an open-source Java library which can be used for learning a number of statistical models from data, such as Bayesian networks, Markov random fields, hybrid random fields, probabilistic decision trees, dependency networks, Gaussian mixture models, Parzen windows, and Nadaraya-Watson conditional density estimators. Along with learning algorithms, some simple inference methods are also implemented. JProGraM is released under the GNU General Public License. It is not yet as polished as I would like it to be, and there is no proper documentation, but it should be especially useful for research purposes. One strong point of the library is the extended support for graphical models with continuous random variables, covering not only standard Gaussian models, but also more recent cutting-edge techniques such as nonparanormal estimation of undirected graphs, scalable dual-tree recursion methods for kernel bandwidth selection (as described e.g. here), or kernel-based random fields. On the other hand, you can use JProGraM to estimate various random network models, such as the Erdős-Rényi, Watts-Strogatz, or Barabási-Albert model, Markov/higher-order exponential random graphs, and Fiedler random graphs and fields, as well as to sample subgraphs from large-scale networks (through random walk, snowball sampling, ...) or compute graph spectra. As additional tools for data analysis, principal components analysis, independent component analysis (through the FastICA library), and (to some extent) data clustering are also supported. Starting with release 12.8, a basic implementation of multilayer feed-forward neural networks has been added, using the backpropagation learning algorithm.

Changes to previous version:

JProGraM 13.2 -- CHANGE LOG

Release date: February 13, 2012

New features: -- Support for Fiedler random graphs/random field models for large-scale networks (ninofreno.graph.fiedler package); -- Various bugfixes and enhancements (especially in the ninofreno.graph and ninofreno.math package).

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Agnostic

Data Formats: None

Tags: Density Estimation, Bayesian Networks, Graphical Models, Markov Random Fields, Dependency Networks, Hybrid Random Fields, Fiedler Random Fields, Random Networks

Archive: download here

Comments

No one has posted any comments yet. Perhaps you'd like to be the first?

Leave a comment

You must be logged in to post comments.