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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
Language Model CascadesCode2
TensorFlow DistributionsCode2
From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of ThoughtCode2
The future of cosmological likelihood-based inference: accelerated high-dimensional parameter estimation and model comparisonCode2
Exact Bayesian Inference on Discrete Models via Probability Generating Functions: A Probabilistic Programming ApproachCode1
DynamicPPL: Stan-like Speed for Dynamic Probabilistic ModelsCode1
SPPL: Probabilistic Programming with Fast Exact Symbolic InferenceCode1
Conditional independence by typingCode1
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