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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 6170 of 273 papers

TitleStatusHype
Financial Data Analysis Using Expert Bayesian Framework For Bankruptcy PredictionCode0
Probabilistic Programs with Stochastic ConditioningCode0
Automatic structured variational inferenceCode0
Automatic Reparameterisation of Probabilistic ProgramsCode0
Accelerating Metropolis-Hastings with Lightweight Inference CompilationCode0
Improved Marginal Unbiased Score Expansion (MUSE) via Implicit DifferentiationCode0
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at ScaleCode0
An Introduction to Probabilistic ProgrammingCode0
Automatically Marginalized MCMC in Probabilistic ProgrammingCode0
A New Distribution-Free Concept for Representing, Comparing, and Propagating Uncertainty in Dynamical Systems with Kernel Probabilistic ProgrammingCode0
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