A theoretical comparison of the data augmentation, marginal augmentation and PX-DA algorithms
From MaRDI portal
Publication:2426614
DOI10.1214/009053607000000569zbMath1155.60031arXiv0804.0671OpenAlexW2075453090MaRDI QIDQ2426614
James P. Hobert, Dobrin Marchev
Publication date: 23 April 2008
Published in: The Annals of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/0804.0671
Bayesian inference (62F15) Continuous-time Markov processes on general state spaces (60J25) Continuous-time Markov processes on discrete state spaces (60J27)
Related Items
Efficient estimation of the link function parameter in a robust Bayesian binary regression model, Sandwich algorithms for Bayesian variable selection, Modified Pólya-Gamma data augmentation for Bayesian analysis of directional data, Estimation in Dirichlet random effects models, Fast Markov Chain Monte Carlo for High-Dimensional Bayesian Regression Models With Shrinkage Priors, Analysis of MCMC algorithms for Bayesian linear regression with Laplace errors, Efficient Bayesian estimation of the multivariate double chain Markov model, Non-reversible guided Metropolis kernel, Cross-fertilizing strategies for better EM mountain climbing and DA field exploration: a graphical guide book, Parameter expansion and efficient inference, A short history of Markov chain Monte Carlo: Subjective recollections from incomplete data, Convergence rates for MCMC algorithms for a robust Bayesian binary regression model, Convergence analysis of the block Gibbs sampler for Bayesian probit linear mixed models with improper priors, Trace class Markov chains for the normal-gamma Bayesian shrinkage model, Improving the convergence properties of the data augmentation algorithm with an application to Bayesian mixture modeling, Spectral properties of MCMC algorithms for Bayesian linear regression with generalized hyperbolic errors, Spectral analytic comparisons for data augmentation, A spectral analytic comparison of trace-class data augmentation algorithms and their sandwich variants, On Monte Carlo methods for Bayesian multivariate regression models with heavy-tailed errors, A New Multinomial Model and a Zero Variance Estimation, Estimating the spectral gap of a trace-class Markov operator, Trace Class Markov Chains for Bayesian Inference with Generalized Double Pareto Shrinkage Priors, A comparison theorem for data augmentation algorithms with applications, A Bayesian multinomial probit model for the analysis of panel choice data, Improving efficiency of data augmentation algorithms using Peskun's theorem, Peskun-Tierney ordering for Markovian Monte Carlo: beyond the reversible scenario, A Pólya–Gamma sampler for a generalized logistic regression, Uncertainty Quantification for Modern High-Dimensional Regression via Scalable Bayesian Methods, A hybrid scan Gibbs sampler for Bayesian models with latent variables
Cites Work
- Markov chains and stochastic stability
- Variance bounding Markov chains
- General state space Markov chains and MCMC algorithms
- On rates of convergence of stochastic relaxation for Gaussian and non- Gaussian distributions
- Geometric ergodicity and hybrid Markov chains
- Honest exploration of intractable probability distributions via Markov chain Monte Carlo.
- Geometric L2 and L1 convergence are equivalent for reversible Markov chains
- Fixed-Width Output Analysis for Markov Chain Monte Carlo
- The Calculation of Posterior Distributions by Data Augmentation
- Seeking efficient data augmentation schemes via conditional and marginal augmentation
- Covariance structure of the Gibbs sampler with applications to the comparisons of estimators and augmentation schemes
- On the applicability of regenerative simulation in Markov chain Monte Carlo
- Generalised Gibbs sampler and multigrid Monte Carlo for Bayesian computation
- Parameter Expansion for Data Augmentation
- Bayesian Analysis of Binary and Polychotomous Response Data
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
