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 151–160 of 273 papers

TitleStatusHype
Universal Marginaliser for Deep Amortised Inference for Probabilistic Programs—0
Using probabilistic programs as proposals—0
Venture: a higher-order probabilistic programming platform with programmable inference—0
Weighted Programming—0
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
Efficient Search-Based Weighted Model Integration—0
Einstein VI: General and Integrated Stein Variational Inference in NumPyro—0
EinSteinVI: General and Integrated Stein Variational Inference—0
Show:102550
← PrevPage 16 of 28Next →

No leaderboard results yet.