Markov Processes for Stochastic Modeling Ebook

Publication: Academic Press
 
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Language: English
Publication Date: September 2008
Pages: 490
File Size: 2453 Kbytes

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$89.95
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ISBN: 9780080922454   Should I buy this Ebook?   Write a Review

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.
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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

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Markov Processes for Stochastic Modeling