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A Java library for performing inference in graphical models (Bayesian and Markov networks) and learning distribution parameters.

Implements Bayesian Logistic Regression for both Gaussian and Laplace Priors. For more information, see Alexander Genkin, David D. Lewis, David Madigan (2004). Large-scale bayesian logistic regression for text categorization.

Class for building and using a Discriminative Multinomial Naive Bayes classifier. For more information see: Jiang Su,Harry Zhang,Charles X. Ling,Stan Matwin: Discriminative Parameter Learning for Bayesian Networks. In: ICML 2008', 2008.

Lazy Bayesian Rules Classifier. The naive Bayesian classifier provides a simple and effective approach to classifier learning, but its attribute independence assumption is often violated in the real world. Lazy Bayesian Rules selectively relaxes the independence assumption, achieving lower error rates over a range of learning tasks. LBR defers processing to classification time, making it a highly efficient and accurate classification algorithm when small numbers of objects are to be classified. For more information, see: Zijian Zheng, G. Webb (2000). Lazy Learning of Bayesian Rules. Machine Learning. 4(1):53-84.