Markov processes for stochastic modeling
Author(s)
Bibliographic Information
Markov processes for stochastic modeling
（Elsevier insights）
Elsevier, 2013
2nd ed
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Note
Previous ed: c2009
Includes bibliographical references (p. [481]-494)
Description and Table of Contents
Description
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.
Table of Contents
Chapter 1: Basic ConceptsChapter 2: Introduction to Markov Processes Chapter 3: Discrete-Time Markov ChainsChapter 4: Continuous-Time Markov Chains Chapter 5: Markovian Queueing Systems Chapter 6: Markov Renewal ProcessesChapter 7: Markovian Arrival Processes Chapter 8: Random Walk Chapter 9: Brownian Motion and Diffusion Processes Chapter 10: Controlled Markov ProcessesChapter 11: Hidden Markov ModelsChapter 12: Markov Point Processes
by "Nielsen BookData"
