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A slowly mixing Markov chain with implications for Gibbs sampling

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  • Matthews, Peter

Abstract

We give a Markov chain that converges to its stationary distribution very slowly. It has the form of a Gibbs sampler running on a posterior distribution of a parameter [theta] given data X. Consequences for Gibbs sampling are discussed.

Suggested Citation

  • Matthews, Peter, 1993. "A slowly mixing Markov chain with implications for Gibbs sampling," Statistics & Probability Letters, Elsevier, vol. 17(3), pages 231-236, June.
    • Handle: RePEc:eee:stapro:v:17:y:1993:i:3:p:231-236
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    Cited by:

    1. Justel, Ana & Peña, Daniel, 1995. "Gibbs sampling will fail in outlier problems with strong masking," DES - Working Papers. Statistics and Econometrics. WS 4203, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Yu Hang Jiang & Tong Liu & Zhiya Lou & Jeffrey S. Rosenthal & Shanshan Shangguan & Fei Wang & Zixuan Wu, 2022. "Convergence Rates of Attractive-Repulsive MCMC Algorithms," Methodology and Computing in Applied Probability, Springer, vol. 24(3), pages 2029-2054, September.
    3. Lahiri, Kajal & Gao, Jian, 2002. "Bayesian analysis of nested logit model by Markov chain Monte Carlo," Journal of Econometrics, Elsevier, vol. 111(1), pages 103-133, November.

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