Better Languages for Statistics: foundations for non-parametric probabilistic programming

Información del proyecto

BLaSt

Identificador del acuerdo de subvención: 864202

DOI

10.3030/864202

Fecha de la firma de la CE 26 Febrero 2020

Fecha de inicio 1 Octubre 2020

Fecha de finalización 30 Septiembre 2026

Financiado con arreglo a

• EXCELLENT SCIENCE - European Research Council (ERC)

Coste total

€ 1 931 178,00

Aportación de la UE

€ 1 931 178,00

Coordinado por

THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD
United Kingdom

Periodic Reporting for period 2 - BLaSt (Better Languages for Statistics: foundations for non-parametric probabilistic programming)

Período documentado: 2022-04-01 hasta 2023-09-30

Resumen del contexto y de los objetivos generales del proyecto

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.

Trabajo realizado desde el comienzo del proyecto hasta el final del período abarcado por el informe y los principales resultados hasta la fecha

The work so far has proposed new semantic foundations for programming languages and probabilistic programs, based on categories of concrete sheaves, monads, and Markov categories.

New results in probability theory have also been achieved: new theorems about hierarchical exchangeability, and new treatments of the quantum analogue of de Finetti's theorem.

We have developed new foundations for gradient methods in probabilistic programming.

We have also developed a new programming library for probabilistic programming, based on the techniques developed.

Avances que van más allá del estado de la técnica e impacto potencial esperado (incluida la repercusión socioeconómica y las implicaciones sociales más amplias del proyecto hasta la fecha)

For the remainder of the project we will derive new methods in programming languages so as to advance the state of the art in statistics and probability theory.

We hope to prove a conjecture precisely relating programming libraries to "exchangeable process" in non-parametric statistics.

We will further develop our probabilistic programming library based on further developments. We hope to extend it with gradient methods for advanced performance.

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