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General Probability & Mathematical Statistics
 A Weak Convergence Approach to the Theory of Large Deviations
ISBN: 978-0-471-07672-8
Hardcover
504 pages
February 1997
US $157.50
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Formulation of Large Deviation Theory in Terms of the Laplace Principle.
First Example: Sanov's Theorem.
Second Example: Mogulskii's Theorem.
Representation Formulas for Other Stochastic Processes.
Compactness and Limit Properties for the Random Walk Model.
Laplace Principle for the Random Walk Model with Continuous Statistics.
Laplace Principle for the Random Walk Model with Discontinuous Statistics.
Laplace Principle for the Empirical Measures of a Markov Chain.
Extensions of the Laplace Principle for the Empirical Measures of a Markov Chain.
Laplace Principle for Continuous-Time Markov Processes with Continuous Statistics.
Appendices.
Bibliography.
Indexes.
First Example: Sanov's Theorem.
Second Example: Mogulskii's Theorem.
Representation Formulas for Other Stochastic Processes.
Compactness and Limit Properties for the Random Walk Model.
Laplace Principle for the Random Walk Model with Continuous Statistics.
Laplace Principle for the Random Walk Model with Discontinuous Statistics.
Laplace Principle for the Empirical Measures of a Markov Chain.
Extensions of the Laplace Principle for the Empirical Measures of a Markov Chain.
Laplace Principle for Continuous-Time Markov Processes with Continuous Statistics.
Appendices.
Bibliography.
Indexes.
