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Existence of ‘nearest-neighbour’ spatial Gibbs models

Published online by Cambridge University Press:  01 July 2016

Etienne Bertin*
Affiliation:
Université Pierre Mendès France
Jean-Michel Billiot*
Affiliation:
Université Pierre Mendès France
Rémy Drouilhet*
Affiliation:
Université Pierre Mendès France
*
Postal address: Labsad, BSHM, Université Pierre Mendès France, 1251 Avenue Centrale, BP 47, 38040 Grenoble Cedex 9, France.
Postal address: Labsad, BSHM, Université Pierre Mendès France, 1251 Avenue Centrale, BP 47, 38040 Grenoble Cedex 9, France.
Postal address: Labsad, BSHM, Université Pierre Mendès France, 1251 Avenue Centrale, BP 47, 38040 Grenoble Cedex 9, France.

Abstract

The present study deals with the existence of ‘nearest-neighbour’ type Gibbs models, introduced by Baddeley and Møller in 1989. In such models, the neighbourhood relation depends on the realization of the process. After giving new sufficient conditions to prove the existence of stationary Gibbs states, we deal with the first-nearest-neighbour model, the triplets Delaunay model, Ord's model and Markov connected component type models.

Type
Stochastic Geometry and Statistical Applications
Copyright
Copyright © Applied Probability Trust 1999 

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References

Baddeley, A. and Møller, J. (1989). Nearest-neighbour Markov point processes and random sets. Int. Statist. Rev. 57, 89121.Google Scholar
Bertin, E., Billiot, J.-M. and Drouilhet, R. (1999). Existence of Delaunay pairwise Gibbs point processes with superstable component. J. Statist. Phys. 95, 719744.Google Scholar
Bertin, E., Billiot, J.-M. and Drouilhet, R. (1999). k-Nearest-neighbour Gibbs point processes. Markov Proc. Rel. Fields 5, 219234.Google Scholar
Bertin, E., Billiot, J.-M. and Drouilhet, R. (1999). Spatial Delaunay Gibbs point processes. Stochastic Models 15, 181199.Google Scholar
Boissonnat, J.-D. and Yvinec, M. (1995). Géométrie Algorithmique. Ediscience International, Paris.Google Scholar
Boots, B. (1986). Using angular properties of Delaunay triangles to evaluate point patterns. Geog. Anal. 18, 250260.Google Scholar
Chayes, J., Chayes, L. and Kotecky, R. (1995). The analysis of the Widom–Rowlinson model by stochastic geometric methods. Commun. Math. Phys. 172, 551569.Google Scholar
Daley, D. and Vere-Jones, D. (1988). An Introduction to the Theory of Point Processes. Springer, New York.Google Scholar
Dobrushin, R. (1969). Gibbsian random field. The general case. Func. Anal. Appl. 3, 2228.Google Scholar
Edelsbrunner, H. (1988). Algorithms in Combinatorial Geometry. Springer, New York.Google Scholar
Feynman, R. (1972). Statistical Mechanics. A Set of Lectures. Benjamin, W. A., Reading, MA.Google Scholar
Georgii, H.-O. (1976). Canonical and grand canonical Gibbs states for continuum systems. Commun. Math. Phys. 48, 3151.Google Scholar
Georgii, H.-O. (1994). Large deviations and the equivalence of ensembles for Gibbsian particle systems with superstable interaction. Prob. Theor. Rel. Fields 99, 171195.Google Scholar
Georgii, H.-O. and Häggström, O. (1996). Phase transition in continuum Potts models. Commun. Math. Phys. 181, 507528.Google Scholar
Glötzl, E., (1980). Locale Energien und Potentiale für Punkprozesse. Math. Nachr. 96, 195206.Google Scholar
Grimmet, G. (1995). The stochastic random-cluster process, and uniqueness of random cluster measures. Ann. Prob. 23, 14611510.CrossRefGoogle Scholar
Gruber, C. and Lebowitz, J. (1975). Equilibrium states for classical systems. Commun. Math. Phys. 41, 1118.Google Scholar
Häggström, O., van Lieshout, M. and Møller, J. (1996). Characterisation results and Markov chain Monte Carlo algorithms including exact simulation for spatial point processes. Technical Report R. 96-2040, Department of Mathematics, Aalborg University. To appear in Bernoulli.%Preprint.Google Scholar
van Hove, L. (1949). Quelques propriétés générales de l'intégrale de configuration d'un système de particules avec interaction. Physica 15, 951961.Google Scholar
Kallenberg, O. (1983). Random Measures. Akademie-Verlag and Academic Press, Berlin, London.Google Scholar
Kendall, W. (1990). A spatial Markov property for nearest-neighbour Markov point processes. J. Appl. Prob. 28, 767778.Google Scholar
Kendall, W. (1998). Perfect simulation for the area-interaction point process. In Probability Towards the Year 2000, eds L. Accardy and C. C. Heyde. Springer, New York, pp. 218334. to appear.Google Scholar
Klein, D. (1982). Dobrushin uniqueness techniques and the decay of correlations in continuum statistical mechanics. Commun. Math. Phys. 86, 227246.Google Scholar
Klein, D. (1984). Convergence of grand canonical Gibbs measures. Commun. Math. Phys. 92, 295308.Google Scholar
Lanford, O. and Ruelle, D. (1969). Observables at infinity and states with support range correlations in statistical mechanics. Commun. Math. Phys. 13, 194215.Google Scholar
Lebowitz, J. and Lieb, E. (1972). Phase transition in continuum classical system with finite interactions. Phys. Lett. 39, 98100.Google Scholar
Miles, R. (1970). On the homogeneous planar Poisson point process. Math. Biosci. 6, 85127.Google Scholar
Møller, J., (1994). Discussion contribution. Scand. J. Statist. 21, 346349.Google Scholar
Møller, J., (1994). Lectures on Random Voronoi Tessellations (Lecture Notes in Statist. 87). Springer, New York.Google Scholar
Møller, J. and Waagepetersen, R. (1998). Markov connected component fields. Adv. Appl. Prob. 30, 135.CrossRefGoogle Scholar
Nguyen, X. and Zessin, H. (1976). Integral and differential characterizations of the Gibbs process. Math. Nachr. 88, 105115.Google Scholar
Preparata, F. and Shamos, M. (1988). Computational Geometry, an Introduction. Springer, New York.Google Scholar
Preston, C. (1976). Random Fields. Springer, Berlin.Google Scholar
Ripley, B. (1977). Modelling spatial patterns (with discussion). J. R. Statist. Soc. B, 39, 172212.Google Scholar
Ruelle, D. (1969). Statistical Mechanics. W. R. Benjamin, New York.Google Scholar
Ruelle, D. (1970). Superstable interactions in classical statistical mechanics. Commun. Math. Phys. 18, 127159.Google Scholar
Ruelle, D. (1971). Existence of a phase transition in a continuous classical system. Phys. Rev. Lett. 27, 10401041.CrossRefGoogle Scholar
Sibson, R. (1978). Locally equiangular triangulations. Computer J. 21, 243245.Google Scholar
Stoyan, D., Kendall, W. and Mecke, J. (1995). Stochastic Geometry and its Applications, 2nd edn. John Wiley, Chichester, UK.Google Scholar