[Practical Markov Chain Monte Carlo]: Comment: Monitoring Convergence of the Gibbs Sampler: Further Experience with the Gibbs Stopper
Lu Cui, Martin A. Tanner, Debajyoti Sinha, William Jackson Hall · Statistical Science · 1992
SMITH, A. F. M. and ROBERTS, G. 0. (1993). Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods (with discussion). J. Roy. Statist. Soc. Ser. B. To appear. SWENDSEN, R. H. and WANG, J. S. (1987). Nonuniversal critical dynamics in Monte Carlo simulations. Phys. Rev. Lett. 58 86-88. TANNER, M. A. and WONG, W. H. (1987). The calculation of posterior distributions by data augmentation (with discussion). J. Amer. Statist. Assoc. 82 528-550. THOMPSON, E. A. and Guo, S. W. (1991). Evaluation of likelihood ratios for complex genetic models. IMA J. Math. Appl. Med. Biol. 8 149-169. TIERNEY, L. (1991). Markov chains for exploring posterior distributions. Technical Report 560, School of Statistics, Univ. Minnesota, T6TH, B. (1986). Persistent random walks in random environment. Probab. Theory Related Fields 71 615-625. WANG, J. S. and SWENDSEN, R. H. (1990). Cluster Monte Carlo algorithms. Phys. A 167 565-579. WEI, G. C. G. and TANNER, M. A. (1990). Calculating the content and the boundary of the highest posterior density region via data augmentation. Biometrika 77 649-652. YOUNES, L. (1988). Estimation and annealing for Gibbsian fields. Ann. Inst. H. Poincare Probab. Statist. 24 269-294.