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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 71–80 of 273 papers

TitleStatusHype
Mixed Nondeterministic-Probabilistic Automata: Blending graphical probabilistic models with nondeterminism—0
Surrogate Likelihoods for Variational Annealed Importance Sampling—0
Simulation Intelligence: Towards a New Generation of Scientific Methods—0
Querying Labelled Data with Scenario Programs for Sim-to-Real Validation—0
Testing Probabilistic Circuits—0
Mapping probability word problems to executable representations—0
3DP3: 3D Scene Perception via Probabilistic ProgrammingCode1
A Scenario-Based Platform for Testing Autonomous Vehicle Behavior Prediction Models in Simulation—0
flip-hoisting: Exploiting Repeated Parameters in Discrete Probabilistic Programs—0
LazyPPL: laziness and types in non-parametric probabilistic programs—0
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