Descripción del proyecto
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:
* to propose new probabilistic programming languages: better languages for statistics;
* to devise new general inference methods for probabilistic programs;
* to build new foundations for probability.
The method is to build on advances on exploiting symmetries in traditional programming lan- guage semantics, by combining this with recent successes in formal semantics and verification for probabilistic programming.