Perfect simulation for image restoration

Stochastic Models

Volumn 23, Issue 3, 2007, Pages 475-487

  Huber, Mark ^a  

^a DUKE UNIVERSITY   (United States)

Author keywords

Autonormal model; Coupling; Image analysis; Perfect simulation

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; IMAGE ANALYSIS; MARKOV PROCESSES; MATHEMATICAL MODELS; SAMPLING;

AUTONORMAL DISTRIBUTION; AUTONORMAL MODELS; PERFECT SIMULATION; SAMPLING ALGORITHMS;

IMAGE RECONSTRUCTION;

EID: 34547459727     PISSN: 15326349     EISSN: 15324214     Source Type: Journal    
DOI: 10.1080/15326340701471117     Document Type: Article

References (17)

1. Aldous, D. Some inequalities for reversible Markov chains. J. London Math. Soc. 1982, 25 (2), 561-576.
2. Besag, J. On the statistical analysis of dirty pictures. J. Roy. Stat. Soc B 1986, 48, 259-302.
3. Breyer, L.A.; Roberts, G. O. Catalytic perfect simulation. Methodology and Computing in Applied Probability 2001, 3 (2), 161-177.
4. Diaconis, P.; Saloff-Coste, L. Comparison theorems for reversible Markov chains. Annals of Applied Probability 1993, 3, 696-730.
5. Doeblin, W. Exposé de la théorie des chains simples constantes de Markov à un nombre fini d'états. Rev. Math. de l'Union Interbalkanique 1933, 2, 77-105.
6. Fill, J.A.; Machida, M.; Murdoch, DJ.; Rosenthal, J.S. Extension of Fill's perfect rejection sampling algorithm to general chains. Random Structures Algorithms 2000, 17, 290-316.
7. Galbusera, P.; Lens, L.; Schenck, T.; Waiyaki, E.; Matthysen, E. Genetic variability and gene flow in the globally, critically-endangered Taita thrush. Conservation Genetics 2000, 1, 45-55.
8. Gelfand, A.E.; Smith, A.F.M. Sampling based approaches to calculating marginal densities. J. Amer. Statist. Assoc. 1990, 85, 398-409.
9. Gibbs, A. Convergence in the wasserstein metric for Markov chain Monte Carlo algorithms with applications to image restoration. Stochastic Model 2004, 20:473-492.
10. Häggström, O.; Steif, J.E. Propp-Wilson algorithms and finitary codings for high noise Markov random fields. Combin. Probab. Computing 2000, 9, 425-439.
11. Jerrum, M. A very simple algorithm for estimating the number of h-colourings of a low-degree graph. SIAM J. Comput. 1995, 22, 1087-1116.
12. Luby, M.; Vigoda, E. Fast convergence of the Gauber dynamics for sampling independent sets. Random Structures Algorithms 1999, 15, 229-241.
13. Murdoch, DJ.; Green, P.J. Exact sampling from a continuous state space. Scand. J. Statist. 1998, 25 (3), 483-502.
14. Propp, J.G.; Wilson, D.B. Exact sampling with coupled Markov chains and applications to statistical mechanics. Random Structures Algorithms 1996, 9 (1-2), 223-252.
15. Sinclair, A. Algorithms for Random Generation and Counting: a Markov Chain Approach; Birkhãuser, 1993.
16. Wilson, D.B. How to couple from the past using a read-once source of randomness. Random. Structures Algorithms 2000, 16 (1), 85-113.
17. Wilson, D.B. Layered multishift coupling for use in perfect sampling algorithms (with a. primer on cftp). Fields Institute Communications 2000, 26, 141-176.