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Graphical Models of Probability
Bayesian Networks, Decision Networks, and Probabilistic Relational Models
Created on 2003-03-06 12:24:25 (#934717), last updated 2009-01-21
63 comments received
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38 Journal Entries, 0 Tags, 0 Memories, 0 Virtual Gifts, 3 Userpics
Basic Info| Location: | Manhattan, Kansas, United States |
|---|---|
| Website: | Bayesian Network tools in Java (BNJ) |
| Membership: | Open |
| Posting Access: | All Members |
AboutThis 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.
![[info]](http://l-stat.livejournal.com/img/community.gif)
bayesnets is moderated by William Hsu (![[info]](http://l-stat.livejournal.com/img/userinfo.gif)
banazir).
BNJ consists of modules that implement:
Current features under development in BNJ include:
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.
bayesnets is moderated by William Hsu (banazir).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: exact deterministic algorithms such as clustering (aka the junction tree or Hugin algorithm) and variable elimination, anytime deterministic approximation algorithms such as conditioning, and stochastic sampling
- Implementations of published algorithms and new algorithms for Bayesian network structure learning
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.
ConnectInterests (60):
abduction, algorithms, applied math, applied mathematics, artificial intelligence, bayesian, bayesian networks, bioinformatics, bucket elimination, clustering, combinatorics, computational intelligence, computer science, connectionism, cutset conditioning, data mining, decision networks, decision theory, dynamic models, education, embedded design, embedded systems, genomics, graduate school, graph theory, graphical models, hidden markov models, ilp, inductive logic programming, inference, intelligent systems, java, java development, java programming, knowledge representation, machine learning, math, mathematics, monte carlo, probabilistic, probabilistic reasoning, probabilistic relational models, probability, randomized algorithms, real-time, real-time systems, relatonal databases, robotics, sampling, simulation, simultaneous localization and mapping, soft computing, statistics, stochastic, structure learning, time series, turbo codes, uncertain reasoning, undergraduate education, variational methods
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