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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 191–200 of 273 papers

TitleStatusHype
A Fairness-aware Hybrid Recommender System—0
A Heavy-Tailed Algebra for Probabilistic Programming—0
The Mathematics of Changing one's Mind, via Jeffrey's or via Pearl's update rule—0
A meta-probabilistic-programming language for bisimulation of probabilistic and non-well-founded type systems—0
A New Approach to Probabilistic Programming Inference—0
Anytime Exact Belief Propagation—0
Applications of Probabilistic Programming (Master's thesis, 2015)—0
A theory of contemplation—0
Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs—0
A Probabilistic Programming Approach To Probabilistic Data Analysis—0
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