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Image of - Continuous-Time Markov Chains and Applications A Singular Perturbation Approach (Stochastic Modelling and Applied Probability)
Continuous-Time Markov Chains and Applications A Singular Perturbation Approach (Stochastic Modelling and Applied Probability)

Continuous-Time Markov Chains and Applications A Singular Perturbation Approach (Stochastic Modelling and Applied Probability)

by Yin

Using a singular perturbation approach, this is a systematic treatment of those systems that naturally arise in queuing theory, control and optimisation, and manufacturing, gathering a number of ideas which were previously scattered throughout the literature. The book presents results on asymptotic expansions of the corresponding probability distributions, functional occupation measures, exponential upper bounds, and asymptotic normality. To bridge the gap between theory and applications, a large portion of the book is devoted to various applications, thus reducing the dimensionality for problems under Markovian disturbances and providing tools for dealing with large-scale and complex real-world situations. Much of this stems from the authors'recent research, presenting results which have not appeared elsewhere. An important reference for researchers in applied mathematics, probability and stochastic processes, operations research, control theory, and optimisation.

Highlights

  • binding-icon

    9780387982441

    ISBN:

  • binding-icon

    Yin

    Author:

  • binding-icon

    387

    Pages:

  • binding-icon

    210 gm

    Weight:

  • langauage-icon

    English

    Language:

  • date-icon

    1997

    Year:

  • edition-icon

    N/A

    Edition:

  • binding-icon

    Hardcover

    Binding:

₹1196

₹1495

Using a singular perturbation approach, this is a systematic treatment of those systems that naturally arise in queuing theory, control and optimisation, and manufacturing, gathering a number of ideas which were previously scattered throughout the literature. The book presents results on asymptotic expansions of the corresponding probability distributions, functional occupation measures, exponential upper bounds, and asymptotic normality. To bridge the gap between theory and applications, a large portion of the book is devoted to various applications, thus reducing the dimensionality for problems under Markovian disturbances and providing tools for dealing with large-scale and complex real-world situations. Much of this stems from the authors'recent research, presenting results which have not appeared elsewhere. An important reference for researchers in applied mathematics, probability and stochastic processes, operations research, control theory, and optimisation.

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