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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 11–20 of 273 papers

TitleStatusHype
Probabilistic Parameter Estimators and Calibration Metrics for Pose Estimation from Image Features—0
Querying Labeled Time Series Data with Scenario Programs—0
Probabilistic Programming with Programmable Variational Inference—0
Data Petri Nets meet Probabilistic Programming (Extended version)—0
The future of cosmological likelihood-based inference: accelerated high-dimensional parameter estimation and model comparisonCode2
Simplifying debiased inference via automatic differentiation and probabilistic programmingCode1
ScenicNL: Generating Probabilistic Scenario Programs from Natural Language—0
COBRA-PPM: A Causal Bayesian Reasoning Architecture Using Probabilistic Programming for Robot Manipulation Under Uncertainty—0
Automated Efficient Estimation using Monte Carlo Efficient Influence Functions—0
BlackJAX: Composable Bayesian inference in JAXCode5
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