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

Showing 161170 of 273 papers

TitleStatusHype
Rotation Invariant Householder Parameterization for Bayesian PCACode0
Modular Deep Probabilistic ProgrammingCode0
A Bayesian Monte Carlo approach for predicting the spread of infectious diseasesCode0
Reversible Jump Probabilistic ProgrammingCode0
The Random Conditional Distribution for Higher-Order Probabilistic Inference0
Applying Probabilistic Programming to Affective ComputingCode0
Efficient Search-Based Weighted Model Integration0
LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable ModelsCode0
Tensor Variable Elimination for Plated Factor Graphs0
ProBO: Versatile Bayesian Optimization Using Any Probabilistic Programming LanguageCode0
Show:102550
← PrevPage 17 of 28Next →

No leaderboard results yet.