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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

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Showing 41–50 of 273 papers

TitleStatusHype
Compiling Stan to Generative Probabilistic Languages and Extension to Deep Probabilistic ProgrammingCode0
Hamiltonian Monte Carlo for Probabilistic Programs with DiscontinuitiesCode0
LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable ModelsCode0
Applying Probabilistic Programming to Affective ComputingCode0
Diffusion models for probabilistic programmingCode0
Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Non-parametric BayesCode0
A Factor Graph Approach to Automated Design of Bayesian Signal Processing AlgorithmsCode0
A Bayesian Monte Carlo approach for predicting the spread of infectious diseasesCode0
Differentiable Quantum Programming with Unbounded LoopsCode0
Automating Model Comparison in Factor GraphsCode0
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