<p>This book gives a systematic treatment of singularly perturbed systems that naturally arise in control and optimization, queueing networks, manufacturing systems, and financial engineering. It presents results on asymptotic expansions of solutions of Komogorov forward and backward equations, prop
Continuous-Time Markov Chains and Applications: A Singular Perturbation Approach
β Scribed by G. George Yin, Qing Zhang (auth.)
- Publisher
- Springer New York
- Year
- 1998
- Tongue
- English
- Leaves
- 357
- Series
- Applications of Mathematics 37
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Table of Contents
Front Matter....Pages i-xv
Front Matter....Pages 1-1
Introduction and Overview....Pages 3-14
Mathematical Preliminaries....Pages 15-24
Markovian Models....Pages 25-50
Front Matter....Pages 51-51
Asymptotic Expansion: Irreducible Generators....Pages 53-78
Asymptotic Normality and Exponential Bounds....Pages 79-109
Asymptotic Expansion: Weak and Strong Interactions....Pages 111-166
Weak and Strong Interactions: Asymptotic Properties and Ramification....Pages 167-217
Front Matter....Pages 219-219
Markov Decision Problems....Pages 221-242
Stochastic Control of Dynamical Systems....Pages 243-276
Numerical Methods for Control and Optimization....Pages 277-298
Back Matter....Pages 299-351
β¦ Subjects
Probability Theory and Stochastic Processes; Calculus of Variations and Optimal Control; Optimization
π SIMILAR VOLUMES
Prologue and Preliminaries: Introduction and overview- Mathematical preliminaries.- Markovian models.- Two-Time-Scale Markov Chains: Asymptotic Expansions of Solutions for Forward Equations.- Occupation Measures: Asymptotic Properties and Ramification.- Asymptotic Expansions of Solutions for Backwa
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This book concerns continuous-time controlled Markov chains, also known as continuous-time Markov decision processes. They form a class of stochastic control problems in which a single decision-maker wishes to optimize a given objective function. This book is also concerned with Markov games, where
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