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arXiv · 2512.23740

Towards representation agnostic probabilistic programming

Ole Fenske·Maximilian Popko·Sebastian Bader·Thomas Kirste
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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.

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Ole Fenske, Maximilian Popko, Sebastian Bader, Thomas Kirste. 2025-12-25. Towards representation agnostic probabilistic programming. https://arxiv.org/abs/2512.23740

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