From metropolis to diffusions: Gibbs states and optimal scaling

Stochastic Processes and their Applications

Volumn 90, Issue 2, 2000, Pages 181-206

  Breyer, L A ^a   Roberts, G O ^a  

^a LANCASTER UNIVERSITY   (United Kingdom)

Author keywords

Hamiltonians; Hybrid algorithms; Markov chain Monte Carlo

Indexed keywords

EID: 0041906743     PISSN: 03044149     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0304-4149(00)00041-7     Document Type: Article

References (9)

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3. Frigessi, A., Martinelli, F., Stander, 1996. J. Computational complexity of Markov chain Monte Carlo methods for finite Markov random fields, Biometrika 84 (1), 1-18.
4. Georgii, H.O., 1988. Gibbs Measures and Phase Transitions, de Gruyter Studies in Mathematics, vol. 9. De Gruyter, Berlin.
5. Nguyen, X.X., Zessin, H., 1979. Ergodic theorem for general dynamic systems. Z. Wahrscheinlichkeitstheorie verw. Gebiete 48, 159-176.
6. Petrov, V., 1995. Limit Theorems of Probability Theory. Sequences of Independent Random Variables, Oxford Studies in Probability. Clarendon Press, Oxford.
7. Da Prato, G., Zabczyk, J., 1996. Ergodicity for Infinite Dimensional Systems. Cambridge University Press, Cambridge.
8. Roberts, G.O., Gelman, A., Gilks, W.R., 1997. Weak convergence and optimal scaling of random walk Metropolis algorithms. Preprint.
9. Smith, A.F.M., Roberts, G.O, 1993. Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods. J. Roy. Statist. Soc. B 55 (1), 3-23.