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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.

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Papers

Showing 4150 of 273 papers

TitleStatusHype
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at ScaleCode0
Exploring Bayesian approaches to eQTL mapping through probabilistic programmingCode0
Functional Tensors for Probabilistic ProgrammingCode0
Applying Probabilistic Programming to Affective ComputingCode0
Differentiable Quantum Programming with Unbounded LoopsCode0
Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic ProgramsCode0
A Factor Graph Approach to Automated Design of Bayesian Signal Processing AlgorithmsCode0
A Bayesian Monte Carlo approach for predicting the spread of infectious diseasesCode0
Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Non-parametric BayesCode0
Diffusion models for probabilistic programmingCode0
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