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 101–110 of 273 papers

TitleStatusHype
EinSteinVI: General and Integrated Stein Variational Inference—0
Bayesian Inference of Temporal Task Specifications from Demonstrations—0
Automatic Generation of Probabilistic Programming from Time Series Data—0
ScenicNL: Generating Probabilistic Scenario Programs from Natural Language—0
Expectation Programming: Adapting Probabilistic Programming Systems to Estimate Expectations Efficiently—0
A Probabilistic Programming Idiom for Active Knowledge Search—0
Declarative Modeling and Bayesian Inference of Dark Matter Halos—0
FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs—0
Fast and Correct Gradient-Based Optimisation for Probabilistic Programming via Smoothing—0
Decision-Making with Complex Data Structures using Probabilistic Programming—0
Show:102550
← PrevPage 11 of 28Next →

No leaderboard results yet.