Includes bibliographical references (pages 244-246) and indexes.
Contents:
Introduction -- Lower bounds and a property of lambda -- Upper bounds I -- Identification and reconciliation of rate functions -- Necessary conditions - bounds on the rate function, invariant measures, irreducibility and recurrence -- Upper bounds II - equivalent analytic conditions -- Upper bounds III - sufficient conditions -- The large deviations principle for empirical measures -- The case when S is countable and P is matrix irreducible -- Examples --Large deviations for vector-valued additive functionals.
Summary:
"The purpose of this book is to study the large deviations for empirical measures and vector-valued additive functionals of Markov chains with general state space. Under suitable recurrence conditions, the ergodic theorem for additive functionals of a Markov chain asserts the almost sure convergence of the averages of a real or vector-valued function of the chain to the mean of the function with respect to the invariant measure. In the case of empirical measures, the ergodic theorem states the almost sure convergence in a suitable sense to the invariant measure. The large deviation theorems provide precise asymptotic estimates at logarithmic level of the probabilities of deviating from the preponderant behavior asserted by the ergodic theorems"--Provided by publisher.
This resource is supported by the Institute of Museum and Library Services under the provisions of the Library Services and Technology Act as administered by State Library of Iowa.