Universal Factor Abstraction in Probabilistic Programming
📝 Original Paper Info
- Title: Towards representation agnostic probabilistic programming- ArXiv ID: 2512.23740
- Date: 2025-12-25
- Authors: Ole Fenske, Maximilian Popko, Sebastian Bader, Thomas Kirste
📝 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.💡 Summary & Analysis
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📊 논문 시각자료 (Figures)
