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A New Method for Coupling Random Fields

Published online by Cambridge University Press:  01 February 2010

L. A. Breyer
Affiliation:
Department of Mathematics and Statistics, Lancaster University, Lancaster LA1 4YF, laird@lbreyer.com
G. O. Roberts
Affiliation:
Department of Mathematics and Statistics, Lancaster University, Lancaster LA1 4YF, g.o.roberts@lancaster.ac.uk

Abstract

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Given a Markov chain, a stochastic flow that simultaneously constructs sample paths started at each possible initial value can be constructed as a composition of random fields. Here, a method is described for coupling flows by modifying an arbitrary field (consistent with the Markov chain of interest) by an independence Metropolis-Hastings iteration. The resulting stochastic flow is shown to have many desirable coalescence properties, regardless of the form of the original flow.

Type
Research Article
Copyright
Copyright © London Mathematical Society 2002

References

1Borovkov, A. A. and Foss, S. G., ‘Stochastically recursive sequences and their generalizations’, Siberian Adv. Math. 2 (1992) 1681.Google Scholar
2Diaconis, P. and Fill, J. A., ‘Strong stationary times via a new form of duality’, Ann. Probab. 18 (1990) 14831522.CrossRefGoogle Scholar
3Fill, J.A., ‘An interruptible algorithm for perfect sampling via Markov chains’, Ann. Appl. Probab. 8 (1998) 131162.CrossRefGoogle Scholar
4Foss, S., and Tweedle, R. L., ‘Perfect simulation and backward coupling’, Comm. Statist. Stochastic Models(special issue in honor of Marcel F.Neuts) 14 (1998) 187203.Google Scholar
5Green, P. J. and Murdoch, D. J. ‘Exact sampling for Bayesian inference: towards general-purpose algorithms’, Bayesian statistics IV (ed. Bernardo, J., Berger, J., Dawid, A. P. and Smith, A. F. M., Oxford University Press, 1999) 301321.Google Scholar
6Lindvall, T., Lectures in the coupling method (John Wiley& Sons, New York, 1992).Google Scholar
7Meyn, S. P. and Tweedie, R. L., Markov chains and stochastic stability (Springer, London, 1993).CrossRefGoogle Scholar
8Murdoch, D. J. and Green, P. J., ‘Exact sampling from a continuous state space’, Scand. J. Statist. 25 (1998) 483502.Google Scholar
9Propp, J., and Wilson, D. B., ‘Exact sampling with coupled Markov chains and applications to statistical mechanics’, Random Structures Algorithms 9 (1996) 223252.3.0.CO;2-O>CrossRefGoogle Scholar
10Roberts, G. O. and Rosenthal, J. S., ‘Quantitative bounds for convergence rates of continuous time Markov processes’, Electron. J. Probab. 1 (1996).Google Scholar
11Roberts, G. O. and Smith, A.F.M., ‘Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods’, J. Roy. Statist. Soc. Ser. B. 55 (1993) 323.Google Scholar
12Stramer, O. and Tweedie, R. L., ‘Self-targeting candidates for Metropolis-Hastings algorithms’, Methodology and Computing in Applied Probability 1 (1999) 307328.CrossRefGoogle Scholar