Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-24T10:36:53.787Z Has data issue: false hasContentIssue false

Chaoticity for Multiclass Systems and Exchangeability Within Classes

Published online by Cambridge University Press:  14 July 2016

Carl Graham*
Affiliation:
École Polytechnique, CNRS
*
Postal address: Centre de Mathématique Appliquées, École Polytechnique, CNRS, Palaiseau, 91128, France. Email address: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Classical results for exchangeable systems of random variables are extended to multiclass systems satisfying a natural partial exchangeability assumption. It is proved that the conditional law of a finite multiclass system, given the value of the vector of the empirical measures of its classes, corresponds to independent uniform orderings of the samples within {each} class, and that a family of such systems converges in law {if and only if} the corresponding empirical measure vectors converge in law. As a corollary, convergence within {each} class to an infinite independent and identically distributed system implies asymptotic independence between {different} classes. A result implying the Hewitt-Savage 0-1 law is also extended.

Type
Research Article
Copyright
Copyright © Applied Probability Trust 2008 

References

[1] Aldous, D. J. (1985). Exchangeability and related topics. In École d'Été de Probabilités de Saint-Flour XIII (Lecture Notes Math. 1117), Springer, Berlin, pp. 1198.Google Scholar
[2] Bellomo, N. and Stöcker, S. (2000). Development of Boltzmann models in mathematical biology. In Modeling in Applied Sciences, eds Bellomo, N. and Pulvirenti, M., Birkhäuser, Boston, MA, pp. 225262.Google Scholar
[3] Cercignani, C., Illner, R. and Pulvirenti, M. (1994). The Mathematical Theory of Dilute Gases (Appl. Math. 106). Springer, New York.Google Scholar
[4] Diaconis, P. and Freedman, D. (1980). Finite exchangeable sequences. Ann. Prob. 8, 745764.Google Scholar
[5] Graham, C. (1992). McKean–Vlasov Ito–Skorohod equations, and nonlinear diffusions with discrete Jump sets. Stoch. Proc. Appl. 40, 6982.CrossRefGoogle Scholar
[6] Graham, C. (2000). Kinetic limits for large communication networks. In Modeling in Applied Sciences, eds Bellomo, N. and Pulvirenti, M., Birkhäuser, Boston, MA, pp. 317370.Google Scholar
[7] Graham, C. and Robert, Ph. (2008). Interacting multi-class transmissions in large stochastic networks. Preprint. Available at http://arxiv.org/labs/0810.0347.Google Scholar
[8] Grünfeld, C. P. (2000). Nonlinar kinetic models with chemical reactions. In Modeling in Applied Sciences, eds Bellomo, N. and Pulvirenti, M., Birkhäuser, Boston, MA, pp. 173224.Google Scholar
[9] Kallenberg, O. (1973). Canonical representations and convergence criteria for processes with interchangeable increments. Z. Wahrscheinlichkeitsth. 27, 2336.Google Scholar
[10] Kingman, J. F. C. (1978). Uses of exchangeability. Ann. Prob. 6, 183197.Google Scholar
[11] Méléard, S. (1996). Asymptotic behaviour of some interacting particle systems; McKean–Vlasov and Boltzmann models. In Probabilistic Models for Nonlinear Partial Differential Equations (Lecture Notes Math. 1627), eds Talay, D. and Tubaro, L., Springer, Berlin, pp. 4295.Google Scholar
[12] Struckmaier, J. (2000). Numerical simulation of the Boltzmann equation by particle methods. In Modeling in Applied Sciences, eds Bellomo, N. and Pulvirenti, M., Birkhäuser, Boston, MA, pp. 371419.Google Scholar
[13] Sznitman, A. S. (1991). Topics in propagation of chaos. In École d'Été de Probabilités de Saint-Flour XIX (Lecture Notes Math. 1464), Springer, Berlin, pp. 165251.Google Scholar