A slowly mixing Markov chain with implications for Gibbs sampling

DOI10.1016/0167-7152(93)90172-FzbMath0777.60064OpenAlexW2024504156MaRDI QIDQ1801885

Yanyan Li

Publication date: 17 August 1993

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0167-7152(93)90172-f

zbMATH Keywords

Markov chainposterior distributioncouplingGibbs samplingmixing rate

Mathematics Subject Classification ID

Discrete-time Markov processes on general state spaces (60J05) Inference from stochastic processes (62M99)

Related Items (6)

Convergence rates of attractive-repulsive MCMC algorithms ⋮ Markov-chain monte carlo: Some practical implications of theoretical results ⋮ Efficiency and Convergence Properties of Slice Samplers ⋮ Tuning tempered transitions ⋮ Possible biases induced by mcmc convergence diagnostics ⋮ Bayesian analysis of nested logit model by Markov chain Monte Carlo.

Cites Work

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• Markov chains for exploring posterior distributions. (With discussion)
• Sampling-Based Approaches to Calculating Marginal Densities
• Large Cliques Elude the Metropolis Process

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