Approximate Predetermined Convergence Properties of the Gibbs Sampler
作者： Gareth O Roberts , Sujit K Sahu
DOI: 10.1198/10618600152627915
关键词:
摘要: This article aims to provide a method for approximately predetermining convergence properties of the Gibbs sampler. is be done by first finding an approximate rate normal approximation target distribution. The rates different implementation strategies sampler are compared find best one. In general, limiting on sequence distributions (approaching limit) not same as Theoretical results given in this justify that under conditions, can approximated well. A number practical examples illustration.
core.ac.uk
jstor.org 
sci-hub.se 参考文章(21)
N. E. Breslow, D. G. Clayton, Approximate inference in generalized linear mixed models Journal of the American Statistical Association. ,vol. 88, pp. 9- 25 ,(1993) , 10.1080/01621459.1993.10594284
SUSAN E. HILLS, ADRIAN F. M. SMITH, Diagnostic plots for improved parameterization in Bayesian inference Biometrika. ,vol. 80, pp. 61- 74 ,(1993) , 10.1093/BIOMET/80.1.61
Steven T. Garren, Richard L. Smith, Estimating the second largest eigenvalue of a Markov transition matrix Bernoulli. ,vol. 6, pp. 215- 242 ,(2000) , 10.2307/3318575
Xiao-Li Meng, David Van Dyk, None, The EM Algorithm-an Old Folk-song Sung to a Fast New Tune Journal of the Royal Statistical Society: Series B (Statistical Methodology). ,vol. 59, pp. 511- 567 ,(1997) , 10.1111/1467-9868.00082
SUJIT K. SAHU, GARETH O. ROBERTS, On convergence of the EM algorithmand the Gibbs sampler Statistics and Computing. ,vol. 9, pp. 55- 64 ,(1999) , 10.1023/A:1008814227332
Gareth O. Roberts, Jeffrey S. Rosenthal, Peter O. Schwartz, Convergence properties of perturbed Markov chains Journal of Applied Probability. ,vol. 35, pp. 1- 11 ,(1998) , 10.1239/JAP/1032192546
M. W. Knuiman, T. P. Speed, Incorporating prior information into the analysis of contingency tables. Biometrics. ,vol. 44, pp. 1061- 1071 ,(1988) , 10.2307/2531735
A. P. Dempster, N. M. Laird, D. B. Rubin, Maximum Likelihood from Incomplete Data Via theEMAlgorithm Journal of the Royal Statistical Society: Series B (Methodological). ,vol. 39, pp. 1- 22 ,(1977) , 10.1111/J.2517-6161.1977.TB01600.X
G.O. Roberts, A.F.M. Smith, Simple conditions for the convergence of the Gibbs sampler and Metropolis-Hastings algorithms Stochastic Processes and their Applications. ,vol. 49, pp. 207- 216 ,(1994) , 10.1016/0304-4149(94)90134-1
Alan E. Gelfand, Adrian F. M. Smith, Sampling-Based Approaches to Calculating Marginal Densities Journal of the American Statistical Association. ,vol. 85, pp. 398- 409 ,(1990) , 10.1080/01621459.1990.10476213