UnBBayes is a probabilistic network framework written in Java. It has both a GUI and an API with inference, sampling, learning and evaluation. It supports Bayesian networks, influence diagrams, MSBN, OOBN, HBN, MEBN/PR-OWL, PRM, structure, parameter and incremental learning.
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- Bayes Net (or Bayesian network)
- Influence Diagrams (or Decision Graphs)
- MSBN (Multiple-Sectioned Bayesian Network)
- OOBN (Object-Oriented Bayesian Network)
- MEBN (Multi-Entity Bayesian Network)
- PR-OWL (Probabilistic Web Ontology Language)
- PR-OWL 2
- UMP-ST (Uncertainty Modeling Process for Semantic Technologies)
- Sampling and Simulation (e.g. Monte Carlo, Gibbs)
- Approximate Inference
- Data Mining
- Plug-in Support
- PRM (Probabilistic Relational Model)
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This is a powerful probabilistic reasoning framework that incorporates a large number of techniques and algorithms. There are plugins for most if not all the major approaches for probabilistic systems, such as BNs, IDs, OOBNs, DBNs, PRMS, MEBNs, PR-OWL, and many others in a list that keeps growing. The learning curve is steep in many cases, which is expected in what is basically a research tool. It is hard to believe that such a powerful framework is freely available to everyone. Kudos for the dedicated team developing it in all their spare time.
The best open source package for working with PGMs. Supports decision nodes and utility nodes (unlike SamIam). Is open source (unlike Geany and SamIam). Many plugins for different things. However, I do wish that it implemented different inference algorithms, such as recursive conditioning and loopy belief propapagation, and had the ability to perform sensitivity analysis.
UnBBayes is a free and easy to use tool for Bayesian Networks. I am using it on my Master Thesis.
Just tried the newer (plugin based) version of unbBayes and glad to see the refinements. Ability to see the complete MTheory and do adjustments by dragging & resizing MFRag panels is cool.
I've been using UnBBayes for several years now, and it has come a long way. I am happy to have an implementation of MEBN. The changes over the past couple of years -- the GUI improvements, the plugin framework, the bug fixes have made it much better. I am really looking forward to seeing PR-OWL 2.0.