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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 51–60 of 273 papers

TitleStatusHype
A Probabilistic Programming Idiom for Active Knowledge Search—0
A Heavy-Tailed Algebra for Probabilistic Programming—0
Bayesian Policy Search for Stochastic Domains—0
Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling—0
A Programmatic and Semantic Approach to Explaining and Debugging Neural Network Based Object Detectors—0
The Mathematics of Changing one's Mind, via Jeffrey's or via Pearl's update rule—0
BayesDB: A probabilistic programming system for querying the probable implications of data—0
Bob and Alice Go to a Bar: Reasoning About Future With Probabilistic Programs—0
A Step from Probabilistic Programming to Cognitive Architectures—0
A Compilation Target for Probabilistic Programming Languages—0
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