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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

Showing 2130 of 273 papers

TitleStatusHype
PPL Bench: Evaluation Framework For Probabilistic Programming LanguagesCode1
Scenic: A Language for Scenario Specification and Data GenerationCode1
SPPL: Probabilistic Programming with Fast Exact Symbolic InferenceCode1
PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic ProgrammingCode1
Inferring Signaling Pathways with Probabilistic ProgrammingCode1
Planning as Inference in Epidemiological ModelsCode1
πVAE: a stochastic process prior for Bayesian deep learning with MCMCCode1
DynamicPPL: Stan-like Speed for Dynamic Probabilistic ModelsCode1
Probabilistic Programming with Densities in SlicStan: Efficient, Flexible and DeterministicCode1
Scenic: A Language for Scenario Specification and Scene GenerationCode1
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