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 21–30 of 273 papers

TitleStatusHype
Inferring Signaling Pathways with Probabilistic ProgrammingCode1
3DP3: 3D Scene Perception via Probabilistic ProgrammingCode1
BayesCard: Revitilizing Bayesian Frameworks for Cardinality EstimationCode1
PPL Bench: Evaluation Framework For Probabilistic Programming LanguagesCode1
RecSim NG: Toward Principled Uncertainty Modeling for Recommender EcosystemsCode1
Scalable Neural-Probabilistic Answer Set ProgrammingCode1
DynamicPPL: Stan-like Speed for Dynamic Probabilistic ModelsCode1
Sequential Monte Carlo Steering of Large Language Models using Probabilistic ProgramsCode1
PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic ProgrammingCode1
TreeFlow: probabilistic programming and automatic differentiation for phylogeneticsCode1
Show:102550
← PrevPage 3 of 28Next →

No leaderboard results yet.