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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

Showing 221230 of 273 papers

TitleStatusHype
Differentiable Quantum Programming with Unbounded LoopsCode0
MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic ProgrammingCode0
Exploring Bayesian approaches to eQTL mapping through probabilistic programmingCode0
Compiling Stan to Generative Probabilistic Languages and Extension to Deep Probabilistic ProgrammingCode0
Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic ProgrammingCode0
Pyro: Deep Universal Probabilistic ProgrammingCode0
Financial Data Analysis Using Expert Bayesian Framework For Bankruptcy PredictionCode0
Sinkhorn AutoEncodersCode0
Towards Verified Stochastic Variational Inference for Probabilistic ProgramsCode0
A New Distribution-Free Concept for Representing, Comparing, and Propagating Uncertainty in Dynamical Systems with Kernel Probabilistic ProgrammingCode0
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