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

Andrew D. Gordon, Microsoft Research and University of Edinburgh

Abstract: 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.

Bio: Andy Gordon is a Principal Researcher at Microsoft Research Cambridge, and is a Professor at the University of Edinburgh. Andy wrote his PhD on input/output in lazy functional programming, and is the proud inventor of Haskell’s “>>=” notation for monads. He’s worked on a range of topics in concurrency, verification, and security, never straying too far from his roots in functional programming. His current passion is deriving machine learning algorithms from F# programs.