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Elements of Applied Stochastic Processes

Elements of Applied Stochastic Processes
Author: U. Narayan Bhat - Gregory K. Miller
ISBN 13: 9780471414421
ISBN 10: 471414425
Edition: 3
Publisher: Wiley-Interscience
Publication Date: 2002-09-06
Format: Hardcover
Pages: 488
List Price: $191.00


"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


This graduate textbook introduces the fundamental theory of stochastic processes—particularly models base on Markov processes—and provides example applications from the fields of computer networking and business management. The third edition adds chapters on statistical inference for Markov chains, statistical inference for simple Markov processes, and Markov chain Monte Carlo. Annotation c. Book News, Inc., Portland, OR