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Reverend Bayes, meet Countess Lovelace: Probabilistic Programming for Machine Learning

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Slides

We propose a marriage of probabilistic functional programming with Bayesian reasoning.  Infer.NET Fun turns the simple succinct syntax of F# into an executable modeling language – you can code up the conditional probability distributions of Bayes' rule using F# array comprehensions with constraints. Write your model in F#. Run it directly to synthesize test datasets and to debug models. Or compile it with Infer.NET for efficient statistical inference.  Hence, efficient algorithms for a range of regression, classification, and specialist learning tasks derive by probabilistic functional programming.

Follow the Discussion

  • AndyGordonAndyGordon

    Hi, you can download the zipfile with the machine learning software I'll talk about, here:

    http://research.microsoft.com/en-us/projects/fun/

    Cheers, Andy

  • Francois RouaixFrancois Rouaix

    No doubt that Infer.NET and Infer.NET Fun are great tools, but they are currently distributed under a very restrictive license. Andy mentions Kaggle competitions, but many of them do require that all software used are unencumbered (either OSS or commercially available software). Even the fact that competitions have rewards may be a violation of the Infer.NET license.

  • Rick MinerichRick Minerich

    Please, please, please release this with a commercially usable license! It would make .NET a viable platform for machine learning.

  • Jack FoxJack Fox

    +1 for a commercial Infer.NET license.

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