Elements of Applied Stochastic Processes

This 3rd edition of the successful Elements of Applied Stochastic Processes improves on the last edition by condensing the material and organising it into a more teachable format. It provides more in-depth coverage of Markov chains and simple Markov process and gives added emphasis to statistical inference in stochastic processes.

• Integration of theory and application offers improved teachability
• Provides a comprehensive introduction to stationary processes and time series analysis
• Integrates a broad set of applications into the text
• Utilizes a wealth of examples from research papers and monographs

“"...provides excellent coverage of the basic topics...Bhat and Miller have provided an excellent text and reference book." ( Interfaces , July/ August 2004) "...an extended and well-written introduction to the theory...of stochastic processes and their applications..." ( Zentralblatt Math , Vol. 1024, 2004)”

"…provides excellent coverage of the basic topics…Bhat and Miller have provided an excellent text and reference book.” (Interfaces, July/ August 2004)

"...an extended and well-written introduction to the theory...of stochastic processes and their applications..." (Zentralblatt Math, Vol. 1024, 2004)

"...besides conveying the concepts of stochastic processes, this book succeeds in providing insight into the reasons why for a particular topic certain lines of investigation are pursued and why certain variables/functions are introduced." (Technometrics, Vol. 45, No. 3, August 2003)

U. NARAYAN BHAT, PhD, is Professor of Statistical Science and Operations Research as well as the Dean of Research and Graduate Studies at Southern Methodist University.

GREGORY K. MILLER, PhD, is Associate Professor of Statistics at Stephen F. Austin State University.

Praise for THE SECOND EDITION

"A valuable contribution . . . rigorous and carefully thought out."

–Zeitschrift fur Operations Research

A state-of-the-art text on stochastic models and their applications

Much has changed in the field of stochastic modeling since the highly successful Second Edition of this popular text. In response, the authors have significantly revised their book to deliver a thoroughly up-to-date overview of the field.

This Third Edition of Elements of Applied Stochastic Processes provides a basic understanding of the fundamental theory of stochastic processes. Topics include Markov chains, and Markov, branching, renewal, and stationary processes, all of which are illustrated with the rich diversity of actual applications. Restructured to enhance the book’s usefulness for practicing professionals, students, and instructors, this edition features two chapters dedicated entirely to applications from journal articles and new material on statistical inference for stochastic processes, with inference on queues as an area of application. Also new is a chapter on simulation and Markov Chain Monte Carlo.

This updated new edition:

• Retains the bridge between theory and application while improving teachability

• Integrates a broad set of applications into the text

• Provides expanded coverage on statistical inference for stochastic processes

• Utilizes a wealth of examples from research papers and monographs

• Offers a comprehensive introduction to stationary processes and time series analysis

Praise for THE SECOND EDITION "A valuable contribution . . . rigorous and carefully thought out." Zeitschrift fur Operations Research A state-of-the-art text on stochastic models and their applications Much has changed in the field of stochastic modeling since the highly successful Second Edition of this popular text. In response, the authors have significantly revised their book to deliver a thoroughly up-to-date overview of the field. This Third Edition of Elements of Applied Stochastic Processes provides a basic understanding of the fundamental theory of stochastic processes. Topics include Markov chains, and Markov, branching, renewal, and stationary processes, all of which are illustrated with the rich diversity of actual applications. Restructured to enhance the book s usefulness for practicing professionals, students, and instructors, this edition features two chapters dedicated entirely to applications from journal articles and new material on statistical inference for stochastic processes, with inference on queues as an area of application. Also new is a chapter on simulation and Markov Chain Monte Carlo. This updated new edition: Retains the bridge between theory and application while improving teachability Integrates a broad set of applications into the text Provides expanded coverage on statistical inference for stochastic processes Utilizes a wealth of examples from research papers and monographs Offers a comprehensive introduction to stationary processes and time series analysis

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