Semantics for probabilistic programming, Dr Chris Heunen

03.10.17, 1pm, Room JCB 1.33B

Abstract: Statistical models in e.g. machine learning are traditionally
expressed in some sort of flow charts. Writing sophisticated models
succintly is much easier in a fully fledged programming language. The
programmer can then rely on generic inference algorithms instead of
having to craft one for each model. Several such higher-order functional
probabilistic programming languages exist, but their semantics, and
hence correctness, are not clear. The problem is that the standard
semantics of probability theory, given by measurable spaces, does not
support function types. I will describe how to get around this.