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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 211–220 of 273 papers

TitleStatusHype
Automatic Generation of Probabilistic Programming from Time Series Data—0
Automatic Inference for Inverting Software Simulators via Probabilistic Programming—0
Automatic Variational Inference in Stan—0
Bachelor's thesis on generative probabilistic programming (in Russian language, June 2014)—0
BayCANN: Streamlining Bayesian Calibration with Artificial Neural Network Metamodeling—0
BayesDB: A probabilistic programming system for querying the probable implications of data—0
Bayesian causal inference via probabilistic program synthesis—0
Bayesian deep learning with hierarchical prior: Predictions from limited and noisy data—0
Bayesian Inference of Temporal Task Specifications from Demonstrations—0
Bayesian Layers: A Module for Neural Network Uncertainty—0
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