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Robustness of Markov processes on large networks

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MacKay, Robert S.. (2011) Robustness of Markov processes on large networks. Journal of Difference Equations and Applications, Vol.17 (No.8). pp. 1155-1167. ISSN 1023-6198

Full text not available from this repository.
Official URL: http://dx.doi.org/10.1080/10236190902976889

Abstract

A metric on the space of probability measures on the state of a large network is introduced, with respect to which the stationary measure of a Markov process on the network is proved under suitable hypotheses to vary uniformly smoothly with parameters and the rate of relaxation to equilibrium to never suddenly decrease.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science > Centre for Complexity Science
Faculty of Science > Mathematics
Library of Congress Subject Headings (LCSH): Markov processes, Probabilities, Stochastic processes, System analysis
Journal or Publication Title: Journal of Difference Equations and Applications
Publisher: Taylor & Francis Inc.
ISSN: 1023-6198
Date: 2011
Volume: Vol.17
Number: No.8
Page Range: pp. 1155-1167
Identification Number: 10.1080/10236190902976889
Status: Peer Reviewed
Publication Status: Published
References: [1] C. Baesens and R.S. MacKay, Exponential localization of linear response in networks with exponentially decaying coupling, Nonlinearity 10 (1997), pp. 931–940. 1166 R.S. MacKay Downloaded by [University of Warwick] at 02:47 07 March 2012 [2] F.G. Ball, R.K. Milne, and G.F. Yeo, Stochastic models for systems of interacting ion channels, IMA J. Med. Biol. 17 (2000), pp. 263–293. [3] G. Birkhoff, Extensions of Jentzch’s theorem, Am. Math. Soc. 85 (1957), pp. 219–227. [4] P. Bre´maud, Markov Chains, Springer, New York, 1999. [5] A. Dembo and O. Zeitouni, Large Deviation Techniques and Applications, Springer, New York, 1998, p. 1993. [6] R.L. Dobrushin, Prescribing a system of random variables by conditional distributions, Theory Prob. Appl. 15 (1970), pp. 458–486. [7] M. Dyer, L.A. Goldberg, and M. Jerrum, Matrix norms and rapid mixing for spin systems, Ann. Appl. Prob. 19 (2009), pp. 71–107. [8] M. Dyer, A. Sinclair, E. Vigoda, and D. Weitz, Mixing in time and space for lattice spin systems: A combinatorial view, Rand. Struct. Algo. 24 (2004), pp. 461–479. [9] A. Eizenberg and Y. Kifer, Large deviations for PCA II, J. Stat. Phys. 117 (2004), pp. 845–889. [10] D.M. Endres and J.E. Schindelin, A new metric for probability distributions, IEEE Trans. Info. Theory 49 (2003), pp. 1858–1860. [11] A.L. Gibbs and F.E. Su, On choosing and bounding probability metrics, Int. Stat. Rev. 70 (2002), pp. 419–435. [12] G.J. Gibson, Markov chain Monte Carlo methods for fitting spatiotemporal stochastic models in plant epidemiology, Appl. Stat. 46 (1997), pp. 215–233. [13] O. Hernandez-Lerma and J.B. Lasserre, Markov Chains and Invariant Probabilities, Birkha¨user, Basel, 2003. [14] I. Kontoyiannis, L.A. Lastras-Montan˜o, and S.P. Meyn, Exponential bounds and stopping rules for MCMC and general Markov chains, ACM Int. Conf. Proc. 180 (2006), p. 45. [15] J.L. Lebowitz, C. Maes, and E.R. Speer, Statistical mechanics of probabilistic cellular automata, J. Stat. Phys. 59 (1990), pp. 117–170. [16] T.M. Liggett, Interacting Particle Systems, Springer, New York, 1985. [17] C. Maes, Coupling interacting particle systems, Rev. Math. Phys. 5 (1993), pp. 457–475. [18] C. Maes and S.B. Shlosman, Ergodicity of probabilitistic cellular automata: A constructive criterion, Commun. Math. Phys. 135 (1991), pp. 233–251; When is an interacting particle system ergodic?, Commun. Math. Phys. 151 (1993), pp. 447–466. [19] R.S. MacKay, Parameter-dependence of Markov processes on large networks, in ECCS’07 Proceedings (CD), J. Jost, ed., 2007, p. 41, (12 pages). [20] D.S. Ornstein and B. Weiss, Statistical properties of chaotic systems, Bull. Am. Math. Soc. 24 (1991), pp. 11–116. [21] T.J. Palmer, A nonlinear dynamical perspective on model error: A proposal for non-local stochastic–dynamic parametrization in weather and climate prediction models, Q. J. Roy. Meteor. Soc. 127 (2001), pp. 279–304. [22] D.W. Stroock, An Introduction to Markov Processes, Springer, New York, 2005. [23] A.L. Toom, N.B. Vasilyev, O.N. Stavskaya, L.G. Mityushin, G.L. Kurdyumov, and S.A. Pirogov, Discrete local Markov systems, in Stochastic Cellular Systems, Ergodicity, Memory and Morphogenesis, R.L. Dobrushin, V.I. Kryukov, and A.L. Toom, eds., Manchester University Press, Manchester, 1990, pp. 1–182. [24] A.M. Vershik, Kantorovich metric: Initial history and little-known applications, J. Math. Sci. 133 (2006), pp. 1410–1417.
URI: http://wrap.warwick.ac.uk/id/eprint/38601

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