Statistical Science

Comment: On Random Scan Gibbs Samplers

Richard A. Levine and George Casella

Full-text: Open access

Article information

Source
Statist. Sci., Volume 23, Number 2 (2008), 192-195.

Dates
First available in Project Euclid: 21 August 2008

Permanent link to this document
https://projecteuclid.org/euclid.ss/1219339111

Digital Object Identifier
doi:10.1214/08-STS252B

Zentralblatt MATH identifier
1327.62067

Citation

Levine, Richard A.; Casella, George. Comment: On Random Scan Gibbs Samplers. Statist. Sci. 23 (2008), no. 2, 192--195. doi:10.1214/08-STS252B. https://projecteuclid.org/euclid.ss/1219339111


Export citation

References

  • Amit, Y. (1996). Convergence properties of the Gibbs sampler for perturbations of Gaussians. Ann. Statist. 24 122–140.
  • Chen, R., Liu, J. S. and Wang, X. (2002). Convergence analyses and comparisons of Markov chain Monte Carlo algorithms in digital communications. IEEE Trans. Signal Process. 50 255–270.
  • Diaconis, P., Khare, K. and Saloff-Coste, L. (2006). Gibbs sampling, exponential families and coupling. Preprint, Dept. Statistics, Stanford Univ.
  • Frigessi, A., Di Stefano, P., Hwang, C.-R. and Sheu, S.-J. (1993). Convergence rates of the Gibbs sampler, the Metropolis algorithm and other single-site updating dynamics. J. Roy. Statist. Soc. Ser. B 55 205–219.
  • Goodman, J. and Sokal, A. (1989). Multigrid Monte Carlo method conceptual foundations. Phys. Rev. D 40 2035–2071.
  • Khare, K. and Zhou, H. (2008). Rates of convergence of some multivariate Markov chains with polynomial eigenfunctions. Technical report, Dept. Statistics, Stanford Univ.
  • Levine, R. A. (2005). A note on Markov chain Monte Carlo sweep strategies. J. Stat. Comput. Simul. 75 253–262.
  • Levine, R. A. and Casella, G. (2006). Optimizing random scan Gibbs samplers. J. Multivariate Anal. 97 2071–2100.
  • Levine, R. A., Yu, Z., Hanley, W. G. and Nitao, J. J. (2005). Implementing random scan Gibbs samplers. Comput. Statist. 20 177–196.
  • Liu, J., Wong, W. and Kong, A. (1995). Covariance structure and convergence rates of the Gibbs sampler with various scans. J. Roy. Statist. Soc. Ser. B 57 157–169.
  • Mira, A. (2001). Ordering and improving performance of Monte Carlo Markov chains. Statist. Sci. 16 340–350.
  • Peskun, P. H. (1973). Optimum Monte-Carlo sampling using Markov chains. Biometrika 60 607–612.
  • Roberts, G. O. and Sahu, S. K. (2001). Approximate predetermined convergence properties of the Gibbs sampler. J. Comput. Graph. Statist. 10 216–229.