Bayesian Networks in Java community 
bayesnets
We have created a LiveJournal community, bayesnets, for discussion of Bayesian Network tools in Java (BNJ), the most comprehensive suite of open-source Java tools for inference and learning using graphical models.
BNJ consists of modules that implement:
We are now developing:
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), please feel free to joinbayesnets for discussions of graphical models.
Cheers,
William Hsu
P.S. : A recent entry contains a questionnaire for community members - I'd like to invite all new and existing subscribers to post a little about their interests in Java development, graphical models, applied mathematics, et cetera (especially features they would like to see in future versions of BNJ).
bayesnets, for discussion of Bayesian Network tools in Java (BNJ), the most comprehensive suite of open-source Java tools for inference and learning using graphical models.BNJ consists of modules that implement:
- ConverterFactory, a GUI-based application for format conversion among Bayesian network formats such as Hugin (.hugin), Microsoft Bayesian Network Editor Format (.dsc), the Microsoft Bayesian Interchange Format (.bif) and XML BN Interchange Format (used by Netica, Hugin, and GeNie), and Ergo.
- Reimplementations of inference algorithms for Bayesian networks: clustering, variable elimination, conditioning (in progress); stochastic sampling
- Implementations of published algorithms and new algorithms for Bayesian network structure learning: K2 (Cooper and Herskovits, 1992), sparse candidate (Friedman et al., 1999), the genetic algorithm wrapper for K2 (GAWK - Hsu, Guo, Joehanes, Perry, Thornton, 2002)
We are now developing:
- 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), please feel free to join
bayesnets for discussions of graphical models.Cheers,
William Hsu
P.S. : A recent entry contains a questionnaire for community members - I'd like to invite all new and existing subscribers to post a little about their interests in Java development, graphical models, applied mathematics, et cetera (especially features they would like to see in future versions of BNJ).
