{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/lf-ppl-a-low-level-first-order-probabilistic","title":"LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models","arxiv_id":"1903.02482","date":"2019-03-06","proceeding":null,"authors":["Yuan Zhou","Bradley J. Gram-Hansen","Tobias Kohn","Tom Rainforth","Hongseok Yang","Frank Wood"],"abstract":"We develop a new Low-level, First-order Probabilistic Programming Language\n(LF-PPL) suited for models containing a mix of continuous, discrete, and/or\npiecewise-continuous variables. The key success of this language and its\ncompilation scheme is in its ability to automatically distinguish parameters\nthe density function is discontinuous with respect to, while further providing\nruntime checks for boundary crossings. This enables the introduction of new\ninference engines that are able to exploit gradient information, while\nremaining efficient for models which are not everywhere differentiable. We\ndemonstrate this ability by incorporating a discontinuous Hamiltonian Monte\nCarlo (DHMC) inference engine that is able to deliver automated and efficient\ninference for non-differentiable models. Our system is backed up by a\nmathematical formalism that ensures that any model expressed in this language\nhas a density with measure zero discontinuities to maintain the validity of the\ninference engine.","url_abs":"http://arxiv.org/abs/1903.02482v1","url_pdf":"http://arxiv.org/pdf/1903.02482v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"lf-ppl-a-low-level-first-order-probabilistic","repo_url":"https://github.com/bradleygramhansen/PyLFPPL","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"probabilistic-programming","task_name":"Probabilistic Programming"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1903.02482","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.02482"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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