Skip to main content
SpringerLink
Log in
Book cover

Introduction to Modeling and Analysis of Stochastic Systems

  • Textbook
  • © 2011

Overview

Authors:
  1. V. G. Kulkarni

    1. , Dept. of Operations Research, University of North Carolina, Chapel Hill, USA

    View author publications

    You can also search for this author in PubMed   Google Scholar

  • Enables readers to develop accurate mathematical models of systems that evolve randomly in time
  • Reader able to use the stochastic models developed in the book to design systems to achieve preferred performance targets
  • Includes large number of examples and detailed case studies that provide an easy way to understand the concepts and the methodologies of stochastic modeling
  • Includes supplementary material: sn.pub/extras

Part of the book series: Springer Texts in Statistics (STS)

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 89.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (7 chapters)

  1. Front Matter

    Pages i-xiii
    Download chapter PDF

  2. Introduction

    • V. G. Kulkarni
    Pages 1-4

  3. Discrete-Time Markov Models

    • V. G. Kulkarni
    Pages 5-58

  4. Poisson Processes

    • V. G. Kulkarni
    Pages 59-83

  5. Continuous-Time Markov Models

    • V. G. Kulkarni
    Pages 85-145

  6. Generalized Markov Models

    • V. G. Kulkarni
    Pages 147-187

  7. Queueing Models

    • V. G. Kulkarni
    Pages 189-245

  8. Brownian Motion

    • V. G. Kulkarni
    Pages 247-280

  9. Back Matter

    Pages 281-313
    Download chapter PDF
Back to top

Keywords

About this book

This is an introductory-level text on stochastic modeling. It is suited for undergraduate students in engineering, operations research, statistics, mathematics, actuarial science, business management, computer science, and public policy. It employs a large number of examples to teach the students to use stochastic models of real-life systems to predict their performance, and use this analysis to design better systems. The book is devoted to the study of important classes of stochastic processes: discrete and continuous time Markov processes, Poisson processes, renewal and regenerative processes, semi-Markov processes, queueing models, and diffusion processes. The book systematically studies the short-term and the long-term behavior, cost/reward models, and first passage times. All the material is illustrated with many examples, and case studies. The book provides a concise review of probability in the appendix. The book emphasizes numerical answers to the problems. A collection of MATLAB programs to accompany the this book can be downloaded from http://www.unc.edu/~vkulkarn/Maxim/maxim.zip. A graphical user interface to access the above files can be downloaded from http://www.unc.edu/~vkulkarn/Maxim/maximgui.zip . The second edition incorporates several changes. First its title reflects the changes in content: the chapters on design and control have been removed. The book now contains several case studies that teach the design principles. Two new chapters have been added. The new chapter on Poisson processes gives more attention to this important class of stochastic processes than the first edition did. The new chapter on Brownian motion reflects its increasing importance as an appropriate model for a variety of real-life situations, including finance.

Reviews

From the reviews of the second edition:

“The author has added a new chapter on Poisson processes and another one on Brownian motion. The discussion is kept on an elementary level and does not require any knowledge from measure theory or advanced calculus. … the text is suitable for an undergraduate course on probabilistic modeling for students from physics, engineering, operations research, computer science, business administration or some related field that needs advanced modeling techniques.” (H. M. Mai, Zentralblatt MATH, Vol. 1222, 2011)

“Suitable for undergraduates in Mathematics, Statistics, Operations Research, Computer Science, Business Administration, Public Policy, etc. This is a very clear and readable text on Markov chains, Poisson processes, continuous time Markov chains, renewal processes, and queuing processes. … The treatment is very clear, intuitive as well as rigorous, without being pedantic, and full of interesting examples and case studies. … The book should be fun to teach from and learn from.” (Jayanta K. Ghosh, International Statistical Review, Vol. 80 (3), 2012)

Authors and Affiliations

  • , Dept. of Operations Research, University of North Carolina, Chapel Hill, USA

    V. G. Kulkarni

About the author

V. G. Kulkarni is Professor in the Department of Statistics and Operations Research in the University of North Carolina, Chapel Hill. He has authored a graduate-level text Modeling and Analysis of Stochastic Systems and dozens of articles on stochastic models of queues, computer and communications systems, and production and supply chain systems. He holds a patent on traffic management in telecommunication networks, and has served on the editorial boards of Operations Research Letters, Stochastic Models, and Queueing Systems and Their Applications.

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation