General over-relaxation Markov chain Monte Carlo algorithms for Gaussian densities

Statistics and Probability Letters

Volumn 52, Issue 2, 2001, Pages 115-124

  Barone, Piero ^a   Sebastiani, Giovanni ^a   Stander, Julian ^b  

^a CNR   (Italy)

^b UNIVERSITY OF PLYMOUTH   (United Kingdom)

Author keywords

Blocking; Image analysis; Rate of convergence; Spectral radius

Indexed keywords

EID: 0041902434     PISSN: 01677152     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-7152(00)00165-6     Document Type: Article

References (14)

1. Barone P., Frigessi A. Improving stochastic relaxation for Gaussian random fields. Probab. Eng. Inform. Sci. 4:1990;369-389.
2. Barone, P., Sebastiani, G., Stander, J., 1998. Over-relaxation methods and coupled Markov chains for Monte Carlo simulation. Technical Report 10/1998, Istituto per le Applicazioni del Calcolo, CNR, Rome, Italy.
3. Besag J., Green P., Higdon D., Mengersen K. Bayesian computing and stochastic systems (with discussion). Statist. Sci. 10:1995;3-66.
4. Foster M.A., Hutchison J.M.S. (Eds.) Practical NMR Imaging. 1987;IRL Press Limited, Oxford.
5. Geyer C.J. Markov chain Monte Carlo maximum likelihood. Keramidas E.M. Computing Science and Statistics: Proceedings of the 23rd Symposium on the Interface. 1991;156-163 Interface Foundation, Fairfax Station.
6. Gilks W.R., Richardson S., Spiegelhalter D.J. (Eds.) Markov Chain Monte Carlo in Practice. 1996;Chapman & Hall, London.
7. Gilks, W.R., Roberts, G.O., 1996. Strategies for improving MCMC. In: Gilks, W.R., Richardson, S., Spiegelhalter, D.J. (Eds.), Markov Chain Monte Carlo in Practice. Chapman & Hall, London, pp. 89-114 (Chapter 6).
8. Green P.J., Han X.-L. Metropolis methods, Gaussian proposals and antithetic variables. Barone P., Frigessi A., Piccioni M. Stochastic Models, Statistical Methods, and Algorithms in Image Analysis. 1992;142-164 Springer, Berlin.
9. Lancaster, P., Tismenesky, M., 1985. The theory of matrices: second edition with applications. Computer Science and Applied Mathematics. Academic Press, Inc., San Diego.
10. Neal, R.M., 1995. Suppressing random walks in Markov chain Monte Carlo using ordered overrelaxation. Technical Report 9508, Department of Statistics, University of Toronto, Canada.
11. Roberts G.O., Sahu S.K. Updating schemes, correlation structure, blocking and parametrization for the Gibbs sampler. J. Roy. Statist. Soc. Ser. B. 59:1997;291-317.
12. Rue, H., 2000, Fast sampling of Gaussian Markov random fields with applications. Preprint Statistics No. 1/2000, Norwegian University of Science and Technology, Trondheim, Norway. Also available from http://www.statslab.cam.ac.uk/ ~ mcmc/ .
13. Stoer J., Bulirsch R. Introduction to Numerical Analysis. 2nd Edition:1993;Springer, New York.
14. Winkler G. Image Analysis, Random Fields and Dynamic Monte Carlo Methods: a Mathematical Introduction. 1995;Springer, Berlin.