Markov Processes for Stochastic Modeling

Markov processes are used to model systems with limited memory. This book discusses topics such as Markovian queuing system, 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.

Autorentext
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.

Klappentext
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. They provide valuable insights into systems design and have become the most widely used tools for performance analysis in many fields of study. This book describes the principles and applications of Markov processes in modelling different real life systems.

Zusammenfassung
"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

Inhalt
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