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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 201–210 of 273 papers

TitleStatusHype
A Probabilistic Programming Idiom for Active Knowledge Search—0
A Programmatic and Semantic Approach to Explaining and DebuggingNeural Network Based Object Detectors—0
A Programmatic and Semantic Approach to Explaining and Debugging Neural Network Based Object Detectors—0
A Scenario-Based Platform for Testing Autonomous Vehicle Behavior Prediction Models in Simulation—0
A Step from Probabilistic Programming to Cognitive Architectures—0
Augur: a Modeling Language for Data-Parallel Probabilistic Inference—0
Augur: Data-Parallel Probabilistic Modeling—0
Automated Efficient Estimation using Monte Carlo Efficient Influence Functions—0
Automated learning with a probabilistic programming language: Birch—0
Automated Variational Inference in Probabilistic Programming—0
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