Markov Processes for Stochastic ModelingBy
- Oliver Ibe, University of Massachusetts, Lowell, USA
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.
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.
Hardbound, 512 Pages
Published: October 2008
Imprint: Academic Press
- PrefaceAcknowledgments1. Basic Concepts 2. Introduction to Markov Processes 3. Discrete-Time Markov Chains 4. Continuous-Time Markov Chains 5. Markovian Queueing Systems 6. Markov Renewal Processes7. Markovian Arrival Processes 8. Random Walk9. Brownian Motion and Diffusion Processes 10. Controlled Markov Processes11. Hidden Markov Models 12. Markov Random Fields 13. Markov Point Processes 14. Markov Chain Monte Carlo ReferencesIndex