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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 261–270 of 273 papers

TitleStatusHype
Dependency Parsing for Weibo: An Efficient Probabilistic Logic Programming Approach—0
Decision-Making with Complex Data Structures using Probabilistic Programming—0
Learning Probabilistic Programs—0
Venture: a higher-order probabilistic programming platform with programmable inference—0
A Compilation Target for Probabilistic Programming Languages—0
Detecting Parameter Symmetries in Probabilistic Models—0
Augur: a Modeling Language for Data-Parallel Probabilistic Inference—0
Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs—0
Declarative Modeling and Bayesian Inference of Dark Matter Halos—0
Automated Variational Inference in Probabilistic Programming—0
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