Summary
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.
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.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/864202 |
| Start date: | 01-10-2020 |
| End date: | 30-09-2025 |
| Total budget - Public funding: | 1 931 178,00 Euro - 1 931 178,00 Euro |
Cordis data
Original description
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.
Status
SIGNEDCall topic
ERC-2019-COGUpdate Date
27-04-2024
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