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

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







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The Discussion

  • User profile image

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

    Cheers, Andy

  • User profile image
    Francois 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.

  • User profile image
    Rick Minerich

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

  • User profile image
    Jack Fox

    +1 for a commercial Infer.NET license.

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