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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 251–260 of 273 papers

TitleStatusHype
WOLFE: An NLP-friendly Declarative Machine Learning Stack—0
Picture: A Probabilistic Programming Language for Scene Perception—0
Automatic Inference for Inverting Software Simulators via Probabilistic Programming—0
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic—0
Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations—0
Particle Gibbs with Ancestor Sampling for Probabilistic Programs—0
Slice Sampling for Probabilistic Programming—0
Declarative Statistical Modeling with Datalog—0
Augur: Data-Parallel Probabilistic Modeling—0
Sublinear-Time Approximate MCMC Transitions for Probabilistic Programs—0
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