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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 111–120 of 273 papers

TitleStatusHype
Expectation Programming: Adapting Probabilistic Programming Systems to Estimate Expectations Efficiently—0
Formal Analysis and Redesign of a Neural Network-Based Aircraft Taxiing System with VerifAI—0
Bayesian Layers: A Module for Neural Network Uncertainty—0
A Probabilistic Programming Idiom for Active Knowledge Search—0
A Heavy-Tailed Algebra for Probabilistic Programming—0
EinSteinVI: General and Integrated Stein Variational Inference—0
ScenicNL: Generating Probabilistic Scenario Programs from Natural Language—0
Graph Tracking in Dynamic Probabilistic Programs via Source Transformations—0
Bayesian Inference of Temporal Task Specifications from Demonstrations—0
Einstein VI: General and Integrated Stein Variational Inference in NumPyro—0
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