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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 51–60 of 273 papers

TitleStatusHype
Diffusion models for probabilistic programmingCode0
Worst-Case Analysis is Maximum-A-Posteriori Estimation—0
Inferring Capabilities from Task Performance with Bayesian Triangulation—0
Pearl's and Jeffrey's Update as Modes of Learning in Probabilistic Programming—0
From Probabilistic Programming to Complexity-based Programming—0
Towards an architectural framework for intelligent virtual agents using probabilistic programming—0
A Heavy-Tailed Algebra for Probabilistic Programming—0
Push: Concurrent Probabilistic Programming for Bayesian Deep LearningCode0
Bayesian Calibration of MEMS AccelerometersCode0
Automating Model Comparison in Factor GraphsCode0
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