{"ID":2825267,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.23740","arxiv_id":"2512.23740","title":"Towards representation agnostic probabilistic programming","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.","short_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...","url_abs":"https://arxiv.org/abs/2512.23740","url_pdf":"https://arxiv.org/pdf/2512.23740v1","authors":"[\"Ole Fenske\",\"Maximilian Popko\",\"Sebastian Bader\",\"Thomas Kirste\"]","published":"2025-12-25T15:51:58Z","proceeding":"cs.PL","tasks":"[\"cs.PL\",\"cs.AI\"]","methods":"[]","has_code":false}