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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.

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Papers

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Showing 11–20 of 273 papers

TitleStatusHype
Nonparametric Hamiltonian Monte CarloCode1
Nonparametric Involutive Markov Chain Monte CarloCode1
Planning as Inference in Epidemiological ModelsCode1
PPL Bench: Evaluation Framework For Probabilistic Programming LanguagesCode1
Inferring Signaling Pathways with Probabilistic ProgrammingCode1
Conditional independence by typingCode1
D3p -- A Python Package for Differentially-Private Probabilistic ProgrammingCode1
Automatic Differentiation Variational InferenceCode1
Exact Bayesian Inference on Discrete Models via Probability Generating Functions: A Probabilistic Programming ApproachCode1
3DP3: 3D Scene Perception via Probabilistic ProgrammingCode1
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