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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 81–90 of 273 papers

TitleStatusHype
DeepRV: pre-trained spatial priors for accelerated disease mapping—0
A Dynamic Programming Algorithm for Inference in Recursive Probabilistic Programs—0
Automatic Generation of Probabilistic Programming from Time Series Data—0
Deployable probabilistic programming—0
Designing Perceptual Puzzles by Differentiating Probabilistic Programs—0
Detecting and Quantifying Malicious Activity with Simulation-based Inference—0
Bachelor's thesis on generative probabilistic programming (in Russian language, June 2014)—0
Detecting Parameter Symmetries in Probabilistic Models—0
BayCANN: Streamlining Bayesian Calibration with Artificial Neural Network Metamodeling—0
Efficient Incremental Belief Updates Using Weighted Virtual Observations—0
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