BLaSt
Financiado
Better Languages for Statistics foundations for non parametric probabilistic pr...
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 w...
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.
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Duración del proyecto: 67 meses
Fecha Inicio: 2020-02-26
Fecha Fin: 2025-09-30
Fecha Fin: 2025-09-30
Línea de financiación: concedida
El organismo H2020 notifico la concesión del proyecto el día 2020-02-26
Línea de financiación objetivo
El proyecto se financió a través de la siguiente ayuda:
ERC-2019-COG:
ERC Consolidator Grant
Cerrada
hace 5 años
Presupuesto
El presupuesto total del proyecto asciende a
2M€
Líder del proyecto
THE CHANCELLOR MASTERS AND SCHOLARS OF THE UN...
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Perfil tecnológico
TRL
4-5
Perfil tecnológico
TRL
4-5
291.50K€ |
Participante