Measure Transformer Semantics for Bayesian Machine Learning

• Published in: Logical methods in computer science 2013-09, Vol.9, Issue 3 (3), p.11
• Main Authors: Borgström, Johannes, Gordon, Andrew D, Greenberg, Michael, Margetson, James, Van Gael, Jurgen
• Format: Article
• Language: English
• Subjects: Bayesian modelling; Computer Science; computer science - artificial intelligence; computer science - logic in computer science; computer science - programming languages; Datavetenskap; Machine learning; Probabilistic programming; Programming languages
• ISSN: 1860-5974; 1860-5974
• DOI: 10.2168/LMCS-9(3:11)2013

The Bayesian approach to machine learning amounts to computing posterior distributions of random variables from a probabilistic model of how the variables are related (that is, a prior distribution) and a set of observations of variables. There is a trend in machine learning towards expressing Bayesian models as probabilistic programs. As a foundation for this kind of programming, we propose a core functional calculus with primitives for sampling prior distributions and observing variables. We define measure-transformer combinators inspired by theorems in measure theory, and use these to give a rigorous semantics to our core calculus. The original features of our semantics include its support for discrete, continuous, and hybrid measures, and, in particular, for observations of zero-probability events. We compile our core language to a small imperative language that is processed by an existing inference engine for factor graphs, which are data structures that enable many efficient inference algorithms. This allows efficient approximate inference of posterior marginal distributions, treating thousands of observations per second for large instances of realistic models.