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probabilistic programming
Also known as: probabilistic programming language, probabilistic programming languages, probabilistic programs, probabilistic programming
Facts (10)
Sources
A Comprehensive Review of Neuro-symbolic AI for Robustness ... link.springer.com Dec 9, 2025 6 facts
referenceProbabilistic programming languages such as Pyro, Stan, and ProbLog allow modelers to create probabilistic generative models that incorporate symbolic structure.
referenceThe paper 'Stan: a probabilistic programming language' was authored by Carpenter, B., Gelman, A., Hoffman, M.D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li, P., and Riddell, A., and published in J. Stat. Softw. 76, 1–32 in 2017.
claimRobots can use probabilistic programs to model uncertainty in their environment while using neural networks to analyze sensor data, allowing the system to perform Bayesian updating and planning that is verifiable at the program level.
claimRecent neuro-symbolic work integrates neural networks with probabilistic programming languages (PPLs) such as Pyro or Stan, allowing symbolic probabilistic models to include neural subroutines and output full probability distributions over answers.
claimThe integration of probabilistic programming languages with deep learning faces challenges, specifically regarding the efficiency of inference when neural likelihoods are involved and the difficulty of allowing gradients to flow through discrete sampling operations.
referenceThe paper 'The design and implementation of probabilistic programming languages' was authored by Goodman, N.D. and Stuhlmüller, A., and published online at dippl.org in 2014.
Understanding LLM Understanding skywritingspress.ca Jun 14, 2024 2 facts
claimThinking can be modelled using probabilistic programs, which provide an expressive representation for commonsense reasoning.
claimMeaning construction can be modelled using large language models (LLMs) that translate natural language utterances into code expressions within a probabilistic programming language.
Track: Poster Session 3 - aistats 2026 virtual.aistats.org 1 fact
claimThe general applicability and robustness of posterior inference algorithms are critical to widely used probabilistic programming languages such as Stan, PyMC, Pyro, and Turing.jl.
Papers - Dr Vaishak Belle vaishakbelle.github.io 1 fact
referenceVaishak Belle authored 'Logic + probabilistic programming + causal laws', published in Royal Society Open Science in 2023.