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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 91–100 of 273 papers

TitleStatusHype
Unifying incidence and prevalence under a time-varying general branching processCode0
Supervised Bayesian Specification Inference from Demonstrations—0
Nonparametric Hamiltonian Monte CarloCode1
Expectation Programming: Adapting Probabilistic Programming Systems to Estimate Expectations Efficiently—0
How To Train Your Program: a Probabilistic Programming Pattern for Bayesian Learning From Data—0
Probabilistic Programming Bots in Intuitive Physics Game Play—0
D3p -- A Python Package for Differentially-Private Probabilistic ProgrammingCode1
RecSim NG: Toward Principled Uncertainty Modeling for Recommender EcosystemsCode1
Meta-Learning an Inference Algorithm for Probabilistic Programs—0
Compositional Semantics for Probabilistic Programs with Exact ConditioningCode0
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