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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

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Showing 251–273 of 273 papers

TitleStatusHype
Better call Saul: Flexible Programming for Learning and Inference in NLPCode0
Inference Compilation and Universal Probabilistic ProgrammingCode0
Bayesian Calibration of MEMS AccelerometersCode0
Probabilistic Data Analysis with Probabilistic ProgrammingCode0
Automating Model Comparison in Factor GraphsCode0
Deep Amortized Inference for Probabilistic ProgramsCode0
Joint Distributions for TensorFlow ProbabilityCode0
Automatic structured variational inferenceCode0
Dataflow Matrix Machines as a Generalization of Recurrent Neural NetworksCode0
Automatic Reparameterisation of Probabilistic ProgramsCode0
Probabilistic programming for birth-death models of evolution using an alive particle filter with delayed samplingCode0
Bayesian Neural NetworksCode0
Accelerating Metropolis-Hastings with Lightweight Inference CompilationCode0
Automatically Marginalized MCMC in Probabilistic ProgrammingCode0
Probabilistic Programs with Stochastic ConditioningCode0
LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable ModelsCode0
Simple, Distributed, and Accelerated Probabilistic ProgrammingCode0
Probabilistic Search for Structured Data via Probabilistic Programming and Nonparametric BayesCode0
Markov Senior -- Learning Markov Junior Grammars to Generate User-specified ContentCode0
ProBO: Versatile Bayesian Optimization Using Any Probabilistic Programming LanguageCode0
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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