Project description
For new, easier probabilistic programming languages
Today’s scientists benefit from the accessibility of various databases. But analysis requires new methods in probabilities reasoning and more precise tools. Probabilistic programming learns from methods of programming languages to apply them in designing and using a special programming language for statistical models. It’s used in Bayesian statistics modelling, mainly for a complex, non-parametric sample space, in which the statistical model can be explained in a precise way, but separately from inference algorithms mostly limited in scope. The EU-funded BLaSt project aims at research that will allow creating a semantic base for new probabilistic programming languages and, especially, for a programming language explaining precisely the non-parametric aspects in the symmetries that emerge.
Objective
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.
Fields of science
Keywords
Programme(s)
Funding Scheme
ERC-COG - Consolidator GrantHost institution
OX1 2JD Oxford
United Kingdom