Markov Processes for Stochastic Modeling
  • Author : Oliver Ibe
  • Release Date : 22 May 2013
  • Publisher : Newnes
  • Genre : Mathematics
  • Pages : 514
  • ISBN 13 : 9780124078390

Markov Processes for Stochastic Modeling Book Summary

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.

Markov Processes for Stochastic Modeling

Markov Processes for Stochastic Modeling

Author : Oliver Ibe
Publisher : Newnes
Genre : Mathematics
Total View : 6752 Views
File Size : 46,8 Mb
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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 ...

Markov Processes for Stochastic Modeling

Markov Processes for Stochastic Modeling

Author : Oliver Ibe
Publisher : Academic Press
Genre : Mathematics
Total View : 8506 Views
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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 ...

An Introduction to Stochastic Modeling

An Introduction to Stochastic Modeling

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Publisher : Academic Press
Genre : Mathematics
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Publisher : Springer
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Publisher : Routledge
Genre : Mathematics
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This book presents a radically new approach to problems of evaluating and optimizing the performance of continuous-time stochastic systems. This approach is based on the use of a family of Markov processes called Piecewise-Deterministic Processes (PDPs) as a general class of stochastic system models. A PDP is a Markov process ...

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Author : Vladimir I. Rotar
Publisher : CRC Press
Genre : Mathematics
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A First Course in Probability with an Emphasis on Stochastic Modeling Probability and Stochastic Modeling not only covers all the topics found in a traditional introductory probability course, but also emphasizes stochastic modeling, including Markov chains, birth-death processes, and reliability models. Unlike most undergraduate-level probability texts, the book also focuses ...