Markov Processes for Stochastic Modeling (2nd Edn) (Elsevier Insights)

Ibe, Oliver

ISBN 10: 0323282954 / ISBN 13: 9780323282956

Published by Elsevier, 2013

About this Item

This is an ex-library book and may have the usual library/used-book markings inside.This book has soft covers. In fair condition, suitable as a study copy. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,900grams, ISBN:9780323282956.

Seller Inventory # 5974010

Bibliographic Details

• Title: Markov Processes for Stochastic Modeling (2nd Edn) (Elsevier Insights)
• Publisher: Elsevier
• Publication Date: 2013
• Language: English
• ISBN 10: 0323282954
• ISBN 13: 9780323282956
• Binding: Soft cover
• Edition: 2nd Edition
• Condition: Fair
• Weight: 900 grams
• Seller catalogs:
  • Mathematics

About this title

Synopsis

Markov processes are processes that have limited memory. In particular, their dependence on the past is only through the previous state. They are used to model the behavior of many systems 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.

Covering a wide range of areas of application of Markov processes, this second edition is revised to highlight the most important aspects as well as the most recent trends and applications of Markov processes. The author spent over 16 years in the industry before returning to academia, and he has applied many of the principles covered in this book in multiple research projects. Therefore, this is an applications-oriented book that also includes enough theory to provide a solid ground in the subject for the reader.

• Presents both the theory and applications of the different aspects of Markov processes
• Includes numerous solved examples as well as detailed diagrams that make it easier to understand the principle being presented
• Discusses different applications of hidden Markov models, such as DNA sequence analysis and speech analysis.

About the Author

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.