Andy Gordon's talk, Reverend Bayes, meet Countess Lovelace: Probabilistic Programming for Machine Learning, is online.
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.
Recent comments
9 weeks 5 days ago
13 weeks 6 days ago
15 weeks 4 days ago
15 weeks 4 days ago
18 weeks 2 days ago
22 weeks 6 days ago
22 weeks 6 days ago
23 weeks 2 days ago
23 weeks 2 days ago
26 weeks 1 day ago