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Large deviations for additive functionals of Markov chains

โœ Scribed by Alejandro D. De Acosta, Peter Ney


Publisher
Amer Mathematical Society
Year
2014
Tongue
English
Leaves
120
Series
Memoirs of the American Mathematical Society 1070
Category
Library

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โœฆ Synopsis


For a Markov chain {X?} with general state space S and f:S?R ?, the large deviation principle for {n ?1 ? ??=1 f(X?)} is proved under a condition on the chain which is weaker than uniform recurrence but stronger than geometric recurrence and an integrability condition on f , for a broad class of initial distributions. This result is extended to the case when f takes values in a separable Banach space. Assuming only geometric ergodicity and under a non-degeneracy condition, a local large deviation result is proved for bounded f. A central analytical tool is the transform kernel, whose required properties, including new results, are established. The rate function in the large deviation results is expressed in terms of the convergence parameter of the transform kernel


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Large Deviations for Markov Chains
โœ Alejandro D. de Acosta ๐Ÿ“‚ Library ๐Ÿ“… 2022 ๐Ÿ› Cambridge University Press ๐ŸŒ English

<span>This book studies 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 averag