A weak convergence approach to the theory of large deviations
This innovative text demonstrates how to employ the well-established linear techniques of weak convergence theory to prove large deviation results. Beginning with a step-by-step development of the approach, the book skillfully guides readers through models of increasing complexity covering a wide va...
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| Format: | Book |
| Language: | English |
| Published: |
New York : Wiley, c1997
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| Series: | Wiley series in probability and statistics
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Table of Contents:
- 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 stockhastic 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