Markov Processes for Stochastic Modeling

Markov Processes for Stochastic Modeling

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Description

Markov processes are used to model systems with limited memory. They are used in many areas including communications systems, transportation networks, image segmentation and analysis, biological systems and DNA sequence analysis, random atomic motion and diffusion in physics, social mobility, population studies, epidemiology, animal and insect migration, queueing systems, resource management, dams, financial engineering, actuarial science, and decision systems.

This book, which is written for upper level undergraduate and graduate students, and researchers, presents a unified presentation of Markov processes. In addition to traditional topics such as Markovian queueing system, the book discusses such topics as continuous-time random walk,correlated random walk, Brownian motion, diffusion processes, hidden Markov models, Markov random fields, Markov point processes and Markov chain Monte Carlo. Continuous-time random walk is currently used in econophysics to model the financial market, which has traditionally been modelled as a Brownian motion. Correlated random walk is popularly used in ecological studies to model animal and insect movement. Hidden Markov models are used in speech analysis and DNA sequence analysis while Markov random fields and Markov point processes are used in image analysis. Thus, the book is designed to have a very broad appeal.
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Product details

  • Hardback | 512 pages
  • 154.94 x 228.6 x 38.1mm | 907.18g
  • Academic Press Inc
  • San Diego, United States
  • English
  • black & white tables, figures
  • 0123744512
  • 9780123744517
  • 1,771,172

Table of contents

Preface
Acknowledgments
1. Basic Concepts
2. Introduction to Markov Processes
3. Discrete-Time Markov Chains
4. Continuous-Time Markov Chains
5. Markovian Queueing Systems
6. Markov Renewal Processes
7. Markovian Arrival Processes
8. Random Walk
9. Brownian Motion and Diffusion Processes
10. Controlled Markov Processes
11. Hidden Markov Models
12. Markov Random Fields
13. Markov Point Processes
14. Markov Chain Monte Carlo
References
Index
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Review Text

"It is a good textbook for students and reference book for researchers and practitioners, it provides an introduction to a wide range of topics including the classical and the most actual ones, and the reader who is interested in more information in any particular topic is advised to consult any of specialized books in the references." -- Laszlo Lakatos (Budapest), Zentralblatt MATH "It is a good textbook for students and reference book for researchers and practitioners, it provides an introduction to a wide range of topics including the classical and the most actual ones, and the reader who is interested in more information in any particular topic is advised to consult any of specialized books in the references." -- Laszlo Lakatos (Budapest), Zentralblatt MATH
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Review quote

"It is a good textbook for students and reference book for researchers and practitioners, it provides an introduction to a wide range of topics including the classical and the most actual ones, and the reader who is interested in more information in any particular topic is advised to consult any of specialized books in the references." -- Laszlo Lakatos (Budapest), Zentralblatt MATH
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About Oliver C. Ibe

Dr Ibe has been teaching at U Mass since 2003. He also has more than 20 years of experience in the corporate world, most recently as Chief Technology Officer at Sineria Networks and Director of Network Architecture for Spike Broadband Corp.
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