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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 31–40 of 273 papers

TitleStatusHype
RecSim NG: Toward Principled Uncertainty Modeling for Recommender EcosystemsCode1
Exploring Bayesian approaches to eQTL mapping through probabilistic programmingCode0
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard ModelCode0
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at ScaleCode0
Compiling Stan to Generative Probabilistic Languages and Extension to Deep Probabilistic ProgrammingCode0
Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Non-parametric BayesCode0
Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic ProgramsCode0
Differentiable Quantum Programming with Unbounded LoopsCode0
Dataflow Matrix Machines as a Generalization of Recurrent Neural NetworksCode0
A Bayesian Monte Carlo approach for predicting the spread of infectious diseasesCode0
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