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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 251260 of 273 papers

TitleStatusHype
Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black BoxCode0
ZhuSuan: A Library for Bayesian Deep LearningCode0
Probabilistic Data Analysis with Probabilistic ProgrammingCode0
Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic ProgramsCode0
Joint Distributions for TensorFlow ProbabilityCode0
Bayesian Calibration of MEMS AccelerometersCode0
Deep Amortized Inference for Probabilistic ProgramsCode0
Automating Model Comparison in Factor GraphsCode0
Automatic structured variational inferenceCode0
Probabilistic programming for birth-death models of evolution using an alive particle filter with delayed samplingCode0
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