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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 211220 of 273 papers

TitleStatusHype
Practical optimal experiment design with probabilistic programs0
Probabilistic Parameter Estimators and Calibration Metrics for Pose Estimation from Image Features0
Probabilistic Planning by Probabilistic Programming0
Probabilistic Programming Bots in Intuitive Physics Game Play0
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard ModelCode0
Machine Teaching of Active Sequential LearnersCode0
Modular Deep Probabilistic ProgrammingCode0
Push: Concurrent Probabilistic Programming for Bayesian Deep LearningCode0
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at ScaleCode0
Diffusion models for probabilistic programmingCode0
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