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 121–130 of 273 papers

TitleStatusHype
Data Petri Nets meet Probabilistic Programming (Extended version)—0
Higher-Order Generalization Bounds: Learning Deep Probabilistic Programs via PAC-Bayes Objectives—0
High Five: Improving Gesture Recognition by Embracing Uncertainty—0
Hijacking Malaria Simulators with Probabilistic Programming—0
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic—0
How To Train Your Program: a Probabilistic Programming Pattern for Bayesian Learning From Data—0
Adversarial Message Passing For Graphical Models—0
Inference Plans for Hybrid Particle Filtering—0
Inferring Capabilities from Task Performance with Bayesian Triangulation—0
Data-driven Sequential Monte Carlo in Probabilistic Programming—0
Show:102550
← PrevPage 13 of 28Next →

No leaderboard results yet.