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Graphical Models of Probability

Bayesian Networks, Decision Networks, and Probabilistic Relational Models

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This community is an open discussion forum on graphical models of probability. It was founded by users and developers of Bayesian Network tools in Java (BNJ), a comprehensive suite of open-source Java tools for inference and learning using graphical models. Anyone with an interest in graphical models for reasoning under uncertainty is invited to join.

bayesnets is moderated by William Hsu (banazir).

BNJ consists of modules that implement:

Current features under development in BNJ include:

  • New representations: relational (probabilistic relational models or PRMs), decision-theoretic (decision networks or influence diagrams), temporal (dynamic Bayesian networks or DBNs; hidden Markov models or HMMs), hybrid continuous state, continuous time

  • New algorithms: PRM structure learning, decision network inference, factored frontier algorithms for DBNs, extensions of exact, sampling-based inference to continuous state and time

  • Bioinformatics applications: Interfaces to software for computational genomics and other tools for computational biology

BNJ is available for download from SourceForge.

If you are interested in theoretical computer science (especially graph theory, applied probability, numerical analysis) and in artificial intelligence (especially machine learning, probabilistic reasoning, and automated reasoning with applications to robotics and bioinformatics), you are welcome to join this community and post here.

Note: If you are interested in adopting BNJ for a course, project, or tutorial, please let the BNJ dev team know by posting here or sending e-mail to kdd-tools-L(AT)www.kddresearch.org. The BNJ development team monitors this LiveJournal and will reply to any questions about the software toolkit, algorithms that have been implemented within it, or applications in education and research.

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