On expectation propagation for generalised, linear and mixed models

DOI10.1111/anzs.12199OpenAlexW2581655336MaRDI QIDQ4639816

Andy S. I. Kim, Matthew P. Wand

Publication date: 11 May 2018

Published in: Australian & New Zealand Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/anzs.12199

zbMATH Keywords

message passingBayesian computingmean field variational Bayesfactor graphs\(\mathsf{Infer.NET}\)

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Bayesian inference (62F15)

Related Items

Factor graph fragmentization of expectation propagation, Patch-Based Image Restoration Using Expectation Propagation, A shared parameter mixture model for longitudinal income data with missing responses and zero rounding, Sufficient dimension reduction for clustered data via finite mixture modelling, Fast and Accurate Binary Response Mixed Model Analysis via Expectation Propagation, Density Estimation via Bayesian Inference Engines

Uses Software

• R
• KernSmooth
• SemiPar
• Infer.NET
• sn
• Stan
• BayesDA
• PRMLT

Cites Work

• Variational inference for generalized linear mixed models using partially noncentered parametrizations
• The explicit form of expectation propagation for a simple statistical model
• Variational inference for count response semiparametric regression
• Asymptotic normality and valid inference for Gaussian variational approximation
• Semiparametric regression during 2003--2007
• Mean field variational Bayes for elaborate distributions
• Using Infer.NET for Statistical Analyses
• Variational Bayesian Inference for Parametric and Nonparametric Regression With Missing Data
• Explaining Variational Approximations
• Graphical Models, Exponential Families, and Variational Inference
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