Towards representation agnostic probabilistic programming

• URL: http://arxiv.org/abs/2512.23740v1
• Date: Thu, 25 Dec 2025 15:51:58 GMT
• Title: Towards representation agnostic probabilistic programming
• Abstract summary: We introduce a factor abstraction with five fundamental operations that serve as a universal interface for manipulating factors regardless of their underlying representation.<n>This enables representation-agnostic probabilistic programming where users can freely mix different representations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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