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

Informacje na temat projektu

BLaSt

Identyfikator umowy o grant: 864202

DOI

10.3030/864202

Data podpisania przez KE 26 Lutego 2020

Data rozpoczęcia 1 Października 2020

Data zakończenia 30 Września 2026

Finansowanie w ramach

• EXCELLENT SCIENCE - European Research Council (ERC)

Koszt całkowity

€ 1 931 178,00

Wkład UE

€ 1 931 178,00

Koordynowany przez

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)

Okres sprawozdawczy: 2022-04-01 do 2023-09-30

Podsumowanie kontekstu i ogólnych celów projektu

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.

Prace wykonane od początku projektu do końca okresu sprawozdawczego oraz najważniejsze dotychczasowe rezultaty

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.

Innowacyjność oraz oczekiwany potencjalny wpływ (w tym dotychczasowe znaczenie społeczno-gospodarcze i szersze implikacje społeczne projektu)

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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