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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

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Showing 251–273 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
Better call Saul: Flexible Programming for Learning and Inference in NLPCode0
Automatic Reparameterisation of Probabilistic ProgramsCode0
Unifying incidence and prevalence under a time-varying general branching processCode0
LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable ModelsCode0
Probabilistic Programs with Stochastic ConditioningCode0
Automatically Marginalized MCMC in Probabilistic ProgrammingCode0
Markov Senior -- Learning Markov Junior Grammars to Generate User-specified ContentCode0
Probabilistic Search for Structured Data via Probabilistic Programming and Nonparametric BayesCode0
ProBO: Versatile Bayesian Optimization Using Any Probabilistic Programming LanguageCode0
Accelerating Metropolis-Hastings with Lightweight Inference CompilationCode0
Applying Probabilistic Programming to Affective ComputingCode0
A Bayesian Monte Carlo approach for predicting the spread of infectious diseasesCode0
Stochastic Probabilistic ProgramsCode0
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