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Markov Processes for Stochastic Modeling
 
 

Markov Processes for Stochastic Modeling, 1st Edition

 
Markov Processes for Stochastic Modeling, 1st Edition,Oliver Ibe,ISBN9780123744517
 
 
 

  

Academic Press

9780123744517 New edition

9780080922454

512

229 X 152

In one comprehensive resource, the book provides the many different applications of Markov Processes to aid upper level undergraduate and graduate students, professionals, practitioners, and researchers.


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Key Features

- Provides the practical, current applications of Markov processes
- Coverage of HMM, Point processes, and Monte Carlo
- Includes enough theory to help students gain throrough understanding of the subject
- Principles can be immediately applied in many specific research projects, saving researchers time
- End of chapter exercises provide reinforcement, practice and increased understanding to the student

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.

Readership

This applications-oriented textbook presents both the theory and applications of the different aspects of Markov processes for advanced undergraduate and graduate students in engineering, science and business for whom mathematics is a problem solving tool.

Oliver 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.

Affiliations and Expertise

University of Massachusetts, Lowell, USA

View additional works by Oliver Ibe

Markov Processes for Stochastic Modeling, 1st Edition

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

Quotes and reviews

"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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