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 181–190 of 273 papers

TitleStatusHype
When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development—0
WOLFE: An NLP-friendly Declarative Machine Learning Stack—0
Worst-Case Analysis is Maximum-A-Posteriori Estimation—0
Paraconsistent Foundations for Probabilistic Reasoning, Programming and Concept Formation—0
A Compilation Target for Probabilistic Programming Languages—0
A Convenient Category for Higher-Order Probability Theory—0
Addressing the IEEE AV Test Challenge with Scenic and VerifAI—0
A Distribution Semantics for Probabilistic Term Rewriting—0
Adversarial Message Passing For Graphical Models—0
A Dynamic Programming Algorithm for Inference in Recursive Probabilistic Programs—0
Show:102550
← PrevPage 19 of 28Next →

No leaderboard results yet.