Infer.NET is a framework for running Bayesian inference in graphical models. You can use it 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.
Recently, they have released their Infer.NET 2.4 Beta 4 with support for F#! It’s pretty interesting how using Infer.NET in F# will be like in this very interesting Bayesian inference framework. I’m very sure syntax and the terseness of the language will start to show once it starts making full use of the language features of F#. Have a go at it. I’ll be playing with this in my limited free time.
You can learn more from their PDC 2009 talk Infer.NET: Building Software with Intelligence. Here’s the synopsis for the talk.
Would you like to write software that can adapt to the user, learn from examples or work with uncertain information? Infer.NET is a machine learning framework that lets you build these capabilities directly into your .NET application. The framework allows you to combine detailed domain knowledge with the latest machine learning algorithms to generate tailored code to solve your problem. An API based on random variables lets you call Infer.NET code from within your application. We provide examples of using Infer.NET in search and gaming.