Comment: ``Gibbs sampling, exponential families, and orthogonal polynomials

DOI10.1214/08-STS252CzbMath1327.62063arXiv0808.3856OpenAlexW2032222454MaRDI QIDQ900454

Alicia A. Johnson, Galin L. Jones

Publication date: 22 December 2015

Published in: Statistical Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0808.3856

Mathematics Subject Classification ID

Sampling theory, sample surveys (62D05) Probability distributions: general theory (60E05) Markov chains (discrete-time Markov processes on discrete state spaces) (60J10)

Related Items (2)

Convergence rates of two-component MCMC samplers ⋮ Exact convergence analysis of the independent Metropolis-Hastings algorithms

Cites Work

• Markov chain Monte Carlo: can we trust the third significant figure?
• General state space Markov chains and MCMC algorithms
• On the Markov chain central limit theorem
• Honest exploration of intractable probability distributions via Markov chain Monte Carlo.
• Bounds on regeneration times and convergence rates for Markov chains
• Geometric ergodicity and perfect simulation
• Renewal theory and computable convergence rates for geometrically erdgodic Markov chains
• A mixture representation of \(\pi\) with applications in Markov chain Monte Carlo and perfect sampling.
• Sufficient burn-in for Gibbs samplers for a hierarchical random effects model.
• Fixed-Width Output Analysis for Markov Chain Monte Carlo
• Minorization Conditions and Convergence Rates for Markov Chain Monte Carlo
• A regeneration proof of the central limit theorem for uniformly ergodic Markov chains

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