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Probabilistic Programming

Probabilistic programming languages are designed to describe probabilistic models and then perform inference in those models. PPLs are closely related to graphical models and Bayesian networks, but are more expressive and flexible.

( Image credit: Michael Betancourt )

Papers

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Showing 1–10 of 273 papers

TitleStatusHype
BlackJAX: Composable Bayesian inference in JAXCode5
SymbolicAI: A framework for logic-based approaches combining generative models and solversCode5
The future of cosmological likelihood-based inference: accelerated high-dimensional parameter estimation and model comparisonCode2
From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of ThoughtCode2
Language Model CascadesCode2
TensorFlow DistributionsCode2
Simplifying debiased inference via automatic differentiation and probabilistic programmingCode1
Scaling Integer Arithmetic in Probabilistic ProgramsCode1
Scalable Neural-Probabilistic Answer Set ProgrammingCode1
Sequential Monte Carlo Steering of Large Language Models using Probabilistic ProgramsCode1
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