Comment: Gibbs Sampling, Exponential Families, and Orthogonal Polynomials
Galin L. Jones and Alicia A. Johnson
Source: Statist. Sci. Volume 23, Number 2 (2008), 183-186.
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References
Baxendale, P. H. (2005). Renewal theory and computable convergence rates for geometrically ergodic Markov chains. Ann. Appl. Probab. 15 700–738.
Mathematical Reviews (MathSciNet):
MR2114987
Digital Object Identifier: doi:10.1214/105051604000000710
Project Euclid: euclid.aoap/1107271665
Flegal, J. M., Haran, M. and Jones, G. L. (2008). Markov chain Monte Carlo: Can we trust the third significant figure? Statist. Sci. To appear.
Hobert, J. P. and Robert, C. P. (2004). A mixture representation of π with applications in Markov chain Monte Carlo and perfect sampling. Ann. Appl. Probab. 14 1295–1305.
Mathematical Reviews (MathSciNet):
MR2071424
Digital Object Identifier: doi:10.1214/105051604000000305
Project Euclid: euclid.aoap/1089736286
Jones, G. L. (2004). On the Markov chain central limit theorem. Probab. Surv. 1 299–320.
Mathematical Reviews (MathSciNet):
MR2068475
Digital Object Identifier: doi:10.1214/154957804100000051
Project Euclid: euclid.ps/1104335301
Jones, G. L., Haran, M., Caffo, B. S. and Neath, R. (2006). Fixed-width output analysis for Markov chain Monte Carlo. J. Amer. Statist. Assoc. 101 1537–1547.
Mathematical Reviews (MathSciNet):
MR2279478
Digital Object Identifier: doi:10.1198/016214506000000492
Jones, G. L. and Hobert, J. P. (2001). Honest exploration of intractable probability distributions via Markov chain Monte Carlo. Statist. Sci. 16 312–334.
Mathematical Reviews (MathSciNet):
MR1888447
Digital Object Identifier: doi:10.1214/ss/1015346317
Project Euclid: euclid.ss/1015346317
Jones, G. L. and Hobert, J. P. (2004). Sufficient burn-in for Gibbs samplers for a hierarchical random effects model. Ann. Statist. 32 784–817.
Mathematical Reviews (MathSciNet):
MR2060178
Digital Object Identifier: doi:10.1214/009053604000000184
Project Euclid: euclid.aos/1083178947
Kendall, W. S. (2004). Geometric ergodicity and perfect simulation. Electron. Commun. Probab. 9 140–151.
Mathematical Reviews (MathSciNet):
MR2108860
Latuszynski, K. (2008). MCMC (ɛ-α)-approximation under drift condition with application to Gibbs samplers for a hierarchical random effects model. Preprint.
Roberts, G. O. and Rosenthal, J. S. (2004). General state space Markov chains and MCMC algorithms. Probab. Surv. 1 20–71.
Mathematical Reviews (MathSciNet):
MR2095565
Digital Object Identifier: doi:10.1214/154957804100000024
Project Euclid: euclid.ps/1099928648
Roberts, G. O. and Tweedie, R. L. (1999). Bounds on regeneration times and convergence rates for Markov chains. Stochastic Process. Appl. 80 211–229.
Mathematical Reviews (MathSciNet):
MR1682243
Digital Object Identifier: doi:10.1016/S0304-4149(98)00085-4
Rosenthal, J. S. (1995). Minorization conditions and convergence rates for Markov chain Monte Carlo. J. Amer. Statist. Assoc. 90 558–566.
Mathematical Reviews (MathSciNet):
MR1340509
Digital Object Identifier: doi:10.2307/2291067
JSTOR: links.jstor.org
