Computer Science / Programming Languages

Towards representation agnostic probabilistic programming

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๐Ÿ“ Original Info

  • Title: Towards representation agnostic probabilistic programming
  • ArXiv ID: 2512.23740
  • Date: 2025-12-25
  • Authors: Ole Fenske, Maximilian Popko, Sebastian Bader, Thomas Kirste

๐Ÿ“ Abstract

Current probabilistic programming languages and tools tightly couple model representations with specific inference algorithms, preventing experimentation with novel representations or mixed discrete-continuous models. We introduce a factor abstraction with five fundamental operations that serve as a universal interface for manipulating factors regardless of their underlying representation. This enables representation-agnostic probabilistic programming where users can freely mix different representations (e.g. discrete tables, Gaussians distributions, sample-based approaches) within a single unified framework, allowing practical inference in complex hybrid models that current toolkits cannot adequately express. CCS Concepts: โ€ข Computing methodologies โ†’ Concurrent programming languages.

๐Ÿ“„ Full Content

...(๋ณธ๋ฌธ ๋‚ด์šฉ์ด ๊ธธ์–ด ์ƒ๋žต๋˜์—ˆ์Šต๋‹ˆ๋‹ค. ์‚ฌ์ดํŠธ์—์„œ ์ „๋ฌธ์„ ํ™•์ธํ•ด ์ฃผ์„ธ์š”.)

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