References

Brooks and Roberts1995

Brooks, S. and Roberts, G. (1995). A review of diagnostic methods for Markov chain Monte Carlo. Technical report, Dept of Pure Maths and Mathematical Statistics, University of Cambridge. (Available via anonymous ftp from: ftp.statslab.cam in directory /pub/mcmc or via NetScape or Mosaic at the URL http://www.statslab.cam.ac.uk/ mcmc/html).

Cowles1994

Cowles, M. K. (1994). Practical issues in gibbs sampler implementation with application to bayesian hierarchical modeling of clinical trial data. PhD thesis, Division of Biostatistics, University of Minnesota.

Cowles and Carlin1995

Cowles, M. K. and Carlin, B. P. (1995). Markov chain Monte Carlo convergence diagnostics: a comparative review. J Amer Statist Assoc, (to appear).

Gelman and Rubin1992

Gelman, A. and Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical Science, 7, 457-72.

Geweke1992

Geweke, J. (1992). Evaluating the accuracy of sampling-based approaches to calcualting posterior moments. In Bayesian Statistics 4, (ed. J. M. Bernardo, J. O. Berger, A. P. Dawid, and A. F. M. Smith). Clarendon Press, Oxford, UK.

Heidelberger and Welch1983

Heidelberger, P. and Welch, P. (1983). Simulation run length control in the presence of an initial transient. Operations Research, 31, 1109-44.

Raftery and Lewis1992a

Raftery, A. L. and Lewis, S. (1992a). Comment: One long run with diagnostics: Implementation strategies for Markov chain Monte Carlo. Statistical Science, 7, 493-7.

Raftery and Lewis1992b

Raftery, A. L. and Lewis, S. (1992b). How many iterations in the Gibbs sampler? In Bayesian Statistics 4, (ed. J. M. Bernardo, J. O. Berger, A. P. Dawid, and A. F. M. Smith), pp. 763-74. Oxford University Press.

Silverman1986

Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis. Chapman and Hall, Bristol.

CODA manual