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.
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