SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 91–100 of 273 papers

TitleStatusHype
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
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
SLASH: Embracing Probabilistic Circuits into Neural Answer Set Programming—0
Detecting and Quantifying Malicious Activity with Simulation-based Inference—0
Show:102550
← PrevPage 10 of 28Next →

No leaderboard results yet.