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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 31–40 of 273 papers

TitleStatusHype
Sequential Monte Carlo Steering of Large Language Models using Probabilistic ProgramsCode1
Augur: a Modeling Language for Data-Parallel Probabilistic Inference—0
A Step from Probabilistic Programming to Cognitive Architectures—0
A meta-probabilistic-programming language for bisimulation of probabilistic and non-well-founded type systems—0
A Scenario-Based Platform for Testing Autonomous Vehicle Behavior Prediction Models in Simulation—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
Addressing the IEEE AV Test Challenge with Scenic and VerifAI—0
A Programmatic and Semantic Approach to Explaining and DebuggingNeural Network Based Object Detectors—0
A Probabilistic Programming Idiom for Active Knowledge Search—0
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