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EU funding (€1,931,178): Better Languages for Statistics: foundations for non-parametric probabilistic programming Hor4 Mar 2020 EU Research and Innovation programme "Horizon"
Overview
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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 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.
Funded Companies:
| Company name | Funding amount |
| The Chancellor, Masters and Scholars of the University of Oxford | €1,931,178 |
Source: https://cordis.europa.eu/project/id/864202
The filing refers to a past date, and does not necessarily reflect the current state. The current state is available on the following page: THE Chancellor Masters AND Scholars OF THE University OF Oxford CHARITY, Oxford.
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