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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 5160 of 273 papers

TitleStatusHype
Improved Marginal Unbiased Score Expansion (MUSE) via Implicit DifferentiationCode0
Bayesian Neural NetworksCode0
Exploring Bayesian approaches to eQTL mapping through probabilistic programmingCode0
Automating Model Comparison in Factor GraphsCode0
Diffusion models for probabilistic programmingCode0
Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black BoxCode0
Automatic structured variational inferenceCode0
Modular Deep Probabilistic ProgrammingCode0
Automatic Reparameterisation of Probabilistic ProgramsCode0
Accelerating Metropolis-Hastings with Lightweight Inference CompilationCode0
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