Elements of Applied Stochastic Processes (Probability & Mathematical Statistics)

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9780471071990: Elements of Applied Stochastic Processes (Probability & Mathematical Statistics)
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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

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From the Inside Flap:

This revised and expanded edition of Elements of Applied Stochastic Processes offers wider coverage of the applications of stochastic processes in various fields. It develops basic concepts and techniques and brings together a sampling of their uses for solving problems arising in queueing, reliability, inventory and computer communications, social and behavioral processes, and business management and time series analysis. The book can be divided into two broad sections. Chapters 1 through 9 treat the theory of Markov, renewal, and stationary processes. Chapters 10 through 21 deal with applications and include six new chapters. In addition to the incorporation of new material in all chapters, major changes in the first half include: the consolidation of the chapter on two-state Markov processes with the chapter on Finite Markov Chains; a new section on graph theoretic methods; new chapters on time-dependent and limiting behavior and on special topics and inference in finite Markov chains; the expansion of the material on stationary processes to include ergodic theorems, spectral density, and linear filters; and the addition of over 200 exercises in the first nine chapters, with answers at the end of the book. Completely new chapters in the second half of the book include: queueing networks, communication and information systems, inventory and storage processes, combat models, Markov models in biological sciences, and stochastic models in traffic flow theory and geological sciences. In addition, the chapter on time series analysis has been updated and expanded, and has been moved to the end of the book to maintain the continuity of treatment of similar models. Elements of Applied Stochastic Processes, 2nd Edition is useful as a classroom text for industrial and systems engineering, operations research, statistics, business, economics, mathematics, and computer science students. It serves as a reference for researchers who use probability models based on Markov processes, renewal processes and time series, and aids consultants who solve problems involving probability models in the societal, industrial, business, and government sectors. Additionally, the up-to-date list of references and a list of articles and books for further reading in applied areas will be a major resource for the applied scientist.

From the Back Cover:

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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Bhat, U. Narayan
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