Convergence analysis of a collapsed Gibbs sampler for Bayesian vector autoregressions

DOI10.1214/21-EJS1800zbMath1476.62056arXiv1907.03170OpenAlexW3124264335MaRDI QIDQ2044318

Karl Oskar Ekvall, Galin L. Jones

Publication date: 9 August 2021

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1907.03170

zbMATH Keywords

Markov chain Monte CarloGibbs samplergeometric ergodicityBayesian vector autoregressionconvergence complexity analysis

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Bayesian inference (62F15) Markov processes: estimation; hidden Markov models (62M05)

Related Items (5)

Convergence rates of two-component MCMC samplers ⋮ Assessing and Visualizing Simultaneous Simulation Error ⋮ Exact convergence analysis for metropolis–hastings independence samplers in Wasserstein distances ⋮ On the limitations of single-step drift and minorization in Markov chain convergence analysis ⋮ On the convergence complexity of Gibbs samplers for a family of simple Bayesian random effects models

Cites Work

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• Markov chain Monte Carlo estimation of quantiles
• Geometric ergodicity of random scan Gibbs samplers for hierarchical one-way random effects models
• Convergence analysis of block Gibbs samplers for Bayesian linear mixed models with \(p>N\)
• On the computational complexity of high-dimensional Bayesian variable selection
• Markov chains and stochastic stability
• Markov chain Monte Carlo: can we trust the third significant figure?
• On the Markov chain central limit theorem
• Some interlacing properties of the Schur complement of a Hermitian matrix
• Honest exploration of intractable probability distributions via Markov chain Monte Carlo.
• Convergence control methods for Markov chain Monte Carlo algorithms
• Geometric ergodicity of Metropolis algorithms
• Strong consistency of multivariate spectral variance estimators in Markov chain Monte Carlo
• Sufficient burn-in for Gibbs samplers for a hierarchical random effects model.
• Convergence complexity analysis of Albert and Chib's algorithm for Bayesian probit regression
• Batch means and spectral variance estimators in Markov chain Monte Carlo
• Geometric ergodicity of Gibbs samplers in Bayesian penalized regression models
• Nonasymptotic bounds on the estimation error of MCMC algorithms
• Markov Chains and De-initializing Processes
• Fixed-Width Output Analysis for Markov Chain Monte Carlo
• Forecasting in vector autoregressions with many predictors
• Geometric convergence and central limit theorems for multidimensional Hastings and Metropolis algorithms
• The Collapsed Gibbs Sampler in Bayesian Computations with Applications to a Gene Regulation Problem
• Convergence Analysis of MCMC Algorithms for Bayesian Multivariate Linear Regression with Non‐Gaussian Errors
• Minorization Conditions and Convergence Rates for Markov Chain Monte Carlo
• Multivariate output analysis for Markov chain Monte Carlo
• High-Dimensional Posterior Consistency in Bayesian Vector Autoregressive Models
• MCMC for Imbalanced Categorical Data
• An Introduction to Matrix Concentration Inequalities

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