ERC Consolidator Grant · 2019
Better Languages for Statistics: foundations for non-parametric probabilistic programming
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…
From the public funding record at EU CORDIS. Describes the funded project, not the reviews below.