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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 8190 of 273 papers

TitleStatusHype
Automatic structured variational inferenceCode0
Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic ProgramsCode0
Modular Deep Probabilistic ProgrammingCode0
MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic ProgrammingCode0
Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyroCode0
Diffusion models for probabilistic programmingCode0
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at ScaleCode0
Financial Data Analysis Using Expert Bayesian Framework For Bankruptcy PredictionCode0
Probabilistic Search for Structured Data via Probabilistic Programming and Nonparametric BayesCode0
ZhuSuan: A Library for Bayesian Deep LearningCode0
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