Convergence analysis of the Gibbs sampler for Bayesian general linear mixed models with improper priors
From MaRDI portal
Publication:741808
DOI10.1214/12-AOS1052zbMath1296.60204arXiv1111.3210MaRDI QIDQ741808
James P. Hobert, Jorge Carlos Román
Publication date: 15 September 2014
Published in: The Annals of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1111.3210
Monte CarloMarkov chainconvergence rategeometric ergodicityposterior proprietygeometric drift condition
Related Items
Dimension free convergence rates for Gibbs samplers for Bayesian linear mixed models, Geometric convergence bounds for Markov chains in Wasserstein distance based on generalized drift and contraction conditions, Bayesian regression analysis of data with random effects covariates from nonlinear longitudinal measurements, Convergence analysis of the block Gibbs sampler for Bayesian probit linear mixed models with improper priors, Geometric ergodicity for Bayesian shrinkage models, On reparametrization and the Gibbs sampler, Fast Monte Carlo Markov chains for Bayesian shrinkage models with random effects, Component-wise Markov chain Monte Carlo: uniform and geometric ergodicity under mixing and composition, Convergence analysis of the Gibbs sampler for Bayesian general linear mixed models with improper priors, Block Gibbs samplers for logistic mixed models: convergence properties and a comparison with full Gibbs samplers, On the propriety of the posterior of hierarchical linear mixed models with flexible random effects distributions, Geometric ergodicity of Gibbs samplers for Bayesian general linear mixed models with proper priors, Wasserstein-based methods for convergence complexity analysis of MCMC with applications
Cites Work
- Unnamed Item
- Unnamed Item
- A spectral analytic comparison of trace-class data augmentation algorithms and their sandwich variants
- Markov chains and stochastic stability
- Convergence analysis of the Gibbs sampler for Bayesian general linear mixed models with improper priors
- Gibbs sampling, exponential families and orthogonal polynomials
- Markov chain Monte Carlo: can we trust the third significant figure?
- General state space Markov chains and MCMC algorithms
- Gibbs sampling for a Bayesian hierarchical general linear model
- Batch means and spectral variance estimators in Markov chain Monte Carlo
- Stability of the Gibbs sampler for Bayesian hierarchical models
- Markov Chains and De-initializing Processes
- Fixed-Width Output Analysis for Markov Chain Monte Carlo
- Covariance structure of the Gibbs sampler with applications to the comparisons of estimators and augmentation schemes
- Markov-chain monte carlo: Some practical implications of theoretical results
- A prior for the variance in hierarchical models
- Prior distributions for variance parameters in hierarchical models (Comment on article by Browne and Draper)