It's a very exciting week for the Infer.NET team as we go live with our first public release, which you can get hold of right now!

This release represents many years of work and we believe it is a significant step forward in how automatic inference is performed. Here's a quick summary of what you get:

• Rich modelling language: support for univariate and multivariate variables, both continuous and discrete. Models can be constructed from a broad range of factors including arithmetic operations, linear algebra, range and positivity constraints, Boolean operators, Dirichlet-Discrete, Gaussian,and mixtures or hierachical mixtures of any of these.
• Multiple inference algorithms: built-in algorithms include expectation propagation, belief propagation (a special case of EP) and variational message passing (variational Bayes).
• Designed for large scale inference: Infer.NET is architected as a compiler allowing for very efficient inference through generated code. In addition, the generated code can be viewed, stepped through, profiled or modified as needed, using standard development tools.
• User-extendable: a plug-in architecture which makes Infer.NET open-ended and adaptable. Probability distributions, factors, message-passing operations and inference algorithms can all be added by the user.

We have written a lot of documentation on how the framework works and how you can add your own components to it.  There are also lots of examples - ranging from simple explanatory tutorials to full applications - which should help you get into Infer.NET and then gain a deeper understanding of how it works.

Although we have spent many months of testing Infer.NET internally, it is inevitable that there will be some teething problems, with the system being used in ways which we have not anticipated.  For this reason, we are initially releasing the system as a 'beta' and we are looking for lots of feedback from the community (i.e. you!) on how well it is working and what you are using it for.  If you do encounter a problem, please raise it on the forum and we will aim to respond as quickly as we can.

We really hope that Infer.NET will prove to be a valuable platform for machine learning researchers and practitioners. Your comments and feedback will help us to make Infer.NET the machine learning solution of choice!

Thanks!

The Infer.NET team