Description

Book Synopsis
Applies the well-developed tools of the theory of weak convergence of probability measures to large deviation analysis--a consistent new approach The theory of large deviations, one of the most dynamic topics in probability today, studies rare events in stochastic systems.

Table of Contents
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.

A Weak Convergence Approach to the Theory of Large Deviations

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    A Hardback by Paul Dupuis, Richard S. Ellis

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      View other formats and editions of A Weak Convergence Approach to the Theory of Large Deviations by Paul Dupuis

      Publisher: Wiley
      Publication Date: 10/03/1997
      ISBN13: 9780471076728, 978-0471076728
      ISBN10:

      Description

      Book Synopsis
      Applies the well-developed tools of the theory of weak convergence of probability measures to large deviation analysis--a consistent new approach The theory of large deviations, one of the most dynamic topics in probability today, studies rare events in stochastic systems.

      Table of Contents
      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.

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