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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 101–110 of 273 papers

TitleStatusHype
Addressing the IEEE AV Test Challenge with Scenic and VerifAI—0
Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling—0
Bayesian Policy Search for Stochastic Domains—0
A Programmatic and Semantic Approach to Explaining and DebuggingNeural Network Based Object Detectors—0
Expectation Programming: Adapting Probabilistic Programming Systems to Estimate Expectations Efficiently—0
Bayesian Layers: A Module for Neural Network Uncertainty—0
A Probabilistic Programming Idiom for Active Knowledge Search—0
FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs—0
Fast and Correct Gradient-Based Optimisation for Probabilistic Programming via Smoothing—0
A Heavy-Tailed Algebra for Probabilistic Programming—0
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