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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 11–20 of 273 papers

TitleStatusHype
Exact Bayesian Inference on Discrete Models via Probability Generating Functions: A Probabilistic Programming ApproachCode1
TreeFlow: probabilistic programming and automatic differentiation for phylogeneticsCode1
Nonparametric Involutive Markov Chain Monte CarloCode1
3DP3: 3D Scene Perception via Probabilistic ProgrammingCode1
Nonparametric Hamiltonian Monte CarloCode1
D3p -- A Python Package for Differentially-Private Probabilistic ProgrammingCode1
RecSim NG: Toward Principled Uncertainty Modeling for Recommender EcosystemsCode1
BayesCard: Revitilizing Bayesian Frameworks for Cardinality EstimationCode1
Spacecraft Collision Risk Assessment with Probabilistic ProgrammingCode1
Conditional independence by typingCode1
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