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 clusteringthrough 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.
One of the interesting aspects of Infer.Net is that you define your problem as a model and Infer.Net creates source code based on that definition, which is then used to generate results.
At this point Infer.Net cannot be used in a commercial application. Whereas, Seth Juarez’s library can be used in a commercial application.