Infer.NET

Infer.NET is a framework for running Bayesian inference in graphical models. It can also be used for probabilistic programming as shown in this video. 

You can use Infer.NET to solve many different kinds of machine learning problems, from standard problems like classification or clustering through to customised solutions to domain-specific problems. Infer.NET has been used in a wide variety of domains including information retrieval, bioinformatics, epidemiology, vision, and many others.

A new feature in Infer.NET 2.5 is Fun, a library turns the simple succinct syntax of F# into a probabilistic modeling language for Bayesian machine learning. You can run your models with F# to compute synthetic data, and you can compile your models with the Infer.NET compiler for efficient inference. See the Infer.NET Fun website for additional information.

Infer.NET 2.5 Beta 2 is now available [April 19, 2013].
See the release change history for details.

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Questions? Suggestions? Please use the forum to provide feedback and to share the ways in which you are using Infer.NET (or send e-mail to infersup@microsoft.com).
Citing Infer.NET If you use Infer.NET as part of your research, please cite us as detailed in the FAQ.

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