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 241250 of 273 papers

TitleStatusHype
Amortized Rejection Sampling in Universal Probabilistic ProgrammingCode0
Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyroCode0
Reversible Jump Probabilistic ProgrammingCode0
Ice Core Dating using Probabilistic ProgrammingCode0
An Introduction to Probabilistic ProgrammingCode0
Robust leave-one-out cross-validation for high-dimensional Bayesian modelsCode0
Improved Marginal Unbiased Score Expansion (MUSE) via Implicit DifferentiationCode0
Rotation Invariant Householder Parameterization for Bayesian PCACode0
Borch: A Deep Universal Probabilistic Programming LanguageCode0
Inference Compilation and Universal Probabilistic ProgrammingCode0
Show:102550
← PrevPage 25 of 28Next →

No leaderboard results yet.