The focus of this book is the probability of rare events deviating from the typical long-run behavior of empirical measures and vector-valued additive functionals of general state space Markov chains asserted by the ergodic theorem. The book is of interest to researchers in probability Theory, Statistical Mechanics, and Statistics.
The focus of this book is the probability of rare events deviating from the typical long-run behavior of empirical measures and vector-valued additive functionals of general state space Markov chains asserted by the ergodic theorem. The book is of interest to researchers in probability Theory, Statistical Mechanics, and Statistics.
Alejandro D. de Acosta is Professor Emeritus in the Department of Mathematics, Applied Mathematics and Statistics at Case Western Reserve University. He has taught at the University of California at Berkeley, Massachusetts Institute of Technology, Universidad Nacional de La Plata and Universidad Nacional de Buenos Aires (Argentina), Instituto Venezolano de Investigaciones Científicas, University of Wisconsin¿Madison, and, since 1983, at Case Western Reserve University. He is a Fellow of the Institute of Mathematical Statistics, and has served on the editorial boards of the Annals of Probability and the Journal of Theoretical Probability. He has published research papers in a number of areas of Probability Theory.
Inhaltsangabe
Preface; 1. Introduction; 2. Lower bounds and a property of lambda; 3. Upper bounds I; 4. Identification and reconciliation of rate functions; 5. Necessary conditions bounds on the rate function, invariant measures, irreducibility and recurrence; 6. Upper bounds II equivalent analytic conditions; 7. Upper bounds III sufficient conditions; 8. The large deviations principle for empirical measures; 9. The case when S is countable and P is matrix irreducible; 10. Examples; 11. Large deviations for vector-valued additive functionals; Appendix A; Appendix B; Appendix C; Appendix D; Appendix E; Appendix F; Appendix G; Appendix H; Appendix I; Appendix J; Appendix K; References; Author index; Subject index.
Preface; 1. Introduction; 2. Lower bounds and a property of lambda; 3. Upper bounds I; 4. Identification and reconciliation of rate functions; 5. Necessary conditions bounds on the rate function, invariant measures, irreducibility and recurrence; 6. Upper bounds II equivalent analytic conditions; 7. Upper bounds III sufficient conditions; 8. The large deviations principle for empirical measures; 9. The case when S is countable and P is matrix irreducible; 10. Examples; 11. Large deviations for vector-valued additive functionals; Appendix A; Appendix B; Appendix C; Appendix D; Appendix E; Appendix F; Appendix G; Appendix H; Appendix I; Appendix J; Appendix K; References; Author index; Subject index.
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