Better languages for statistics: foundations for non-parametric probabilistic programming

BLAST is an ERC funded project (consolidator grant) that will run from Oct 2020 until Sept 2025. The project will employ three postdoctoral researchers and support two PhD students. The PI is Sam Staton. Please get in touch if you'd like further details about the project or openings.

Official project abstract

Probabilistic programming is a powerful method for Bayesian statistical modelling, particularly where the sample space is complex or unbounded (non-parametric). This is because the statistical model can be described clearly in a way that is precise but separate from inference algorithms. It accommodates complex models in such a way that outcomes are still explainable. The objective of the proposed research is to develop a semantic foundation for probabilistic programming that properly explains the non-parametric aspects, particularly the symmetries that arise there. There are three ultimate goals:

The method is to build on advances on exploiting symmetries in traditional programming language semantics, by combining this with recent successes in formal semantics and verification for probabilistic programming.

See my homepage for a list of publications giving an idea of the kinds of things we have worked on so far, and my current students and RAs.