A Weak Convergence Approach to the Theory of Large DeviationsISBN: 978-0-471-07672-8
Hardcover
504 pages
February 1997
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Formulation of Large Deviation Theory in Terms of the LaplacePrinciple.
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 ContinuousStatistics.
Laplace Principle for the Random Walk Model with DiscontinuousStatistics.
Laplace Principle for the Empirical Measures of a MarkovChain.
Extensions of the Laplace Principle for the Empirical Measures of aMarkov Chain.
Laplace Principle for Continuous-Time Markov Processes withContinuous 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 ContinuousStatistics.
Laplace Principle for the Random Walk Model with DiscontinuousStatistics.
Laplace Principle for the Empirical Measures of a MarkovChain.
Extensions of the Laplace Principle for the Empirical Measures of aMarkov Chain.
Laplace Principle for Continuous-Time Markov Processes withContinuous Statistics.
Appendices.
Bibliography.
Indexes.