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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 61–70 of 273 papers

TitleStatusHype
Compartmental Models for COVID-19 and Control via Policy Interventions—0
Complex Coordinate-Based Meta-Analysis with Probabilistic Programming—0
A Compilation Target for Probabilistic Programming Languages—0
BayCANN: Streamlining Bayesian Calibration with Artificial Neural Network Metamodeling—0
Bachelor's thesis on generative probabilistic programming (in Russian language, June 2014)—0
A theory of contemplation—0
DeepRV: pre-trained spatial priors for accelerated disease mapping—0
Applications of Probabilistic Programming (Master's thesis, 2015)—0
Automatic Variational Inference in Stan—0
Deep Probabilistic Programming Languages: A Qualitative Study—0
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