A stochastic variational framework for fitting and diagnosing generalized linear mixed models

DOI10.1214/14-BA885zbMath1327.62167arXiv1208.4949OpenAlexW3102845826MaRDI QIDQ899068

Linda S. L. Tan, David J. Nott

Publication date: 21 December 2015

Published in: Bayesian Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1208.4949

zbMATH Keywords

stochastic approximationvariational Bayeshierarchical modelsnonconjugate variational message passingconflict diagnosticsidentify divergent units

Mathematics Subject Classification ID

Bayesian inference (62F15) Generalized linear models (logistic models) (62J12) Diagnostics, and linear inference and regression (62J20)

Related Items (7)

Distributed Bayesian Inference in Linear Mixed-Effects Models ⋮ Variational inference for sparse spectrum Gaussian process regression ⋮ Fast and accurate variational inference for models with many latent variables ⋮ Gaussian variational approximation with sparse precision matrices ⋮ Checking for prior-data conflict using prior-to-posterior divergences ⋮ Stochastic variational inference for large-scale discrete choice models using adaptive batch sizes ⋮ Functional models for longitudinal data with covariate dependent smoothness

Uses Software

• R
• MASS (R)
• R2WinBUGS
• BUGS
• aplore3
• PRMLT

Cites Work

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