Скачать или смотреть [OOPSLA'25] Multi-Language Probabilistic Programming

Multi-Language Probabilistic Programming (Video, OOPSLA1 2025)
Sam Stites, John M. Li, and Steven Holtzen
(Northeastern University, USA; Northeastern University, USA; Northeastern University, USA)

Abstract: There are many different probabilistic programming languages that are specialized to specific kinds of probabilistic programs. From a usability and scalability perspective, this is undesirable: today, probabilistic programmers are forced up-front to decide which language they want to use and cannot mix-and-match different languages for handling heterogeneous programs. To rectify this, we seek a foundation for sound interoperability for probabilistic programming languages: just as today’s Python programmers can resort to low-level C programming for performance, we argue that probabilistic programmers should be able to freely mix different languages for meeting the demands of heterogeneous probabilistic programming environments. As a first step towards this goal, we introduce MultiPPL, a probabilistic multi-language that enables programmers to interoperate between two different probabilistic programming languages: one that leverages a high-performance exact discrete inference strategy, and one that uses approximate importance sampling. We give a syntax and semantics for MultiPPL, prove soundness of its inference algorithm, and provide empirical evidence that it enables programmers to perform inference on complex heterogeneous probabilistic programs and flexibly exploits the strengths and weaknesses of two languages simultaneously.

Article: https://doi.org/10.1145/3720482

Supplementary archive: https://doi.org/10.5281/zenodo.14593465 (Badges: Artifacts Available, Artifacts Evaluated — Reusable, Results Reproduced)

ORCID: https://orcid.org/0000-0003-0935-1010, https://orcid.org/0000-0003-2130-5092, https://orcid.org/0000-0002-8190-5412

Video Tags: Multi-language semantics, probabilistic programming, Bayesian inference, oopslaa25main-p214-p, doi:10.1145/3720482, doi:10.5281/zenodo.14593465, orcid:0000-0003-0935-1010, orcid:0000-0003-2130-5092, orcid:0000-0002-8190-5412, Artifacts Available, Artifacts Evaluated — Reusable, Results Reproduced

Presentation at the OOPSLA1 2025 conference, October 12–18, https://2025.splashcon.org/track/OOPSLA
Sponsored by ACM SIGPLAN,