Markov Processes for Stochastic Modeling - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Markov Processes for Stochastic Modeling write by Oliver Ibe. This book was released on 2013-05-22. Markov Processes for Stochastic Modeling available in PDF, EPUB and Kindle. 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 - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Markov Processes for Stochastic Modeling write by Masaaki Kijima. This book was released on 2013-12-19. Markov Processes for Stochastic Modeling available in PDF, EPUB and Kindle. This book presents an algebraic development of the theory of countable state space Markov chains with discrete- and continuous-time parameters. A Markov chain is a stochastic process characterized by the Markov prop erty that the distribution of future depends only on the current state, not on the whole history. Despite its simple form of dependency, the Markov property has enabled us to develop a rich system of concepts and theorems and to derive many results that are useful in applications. In fact, the areas that can be modeled, with varying degrees of success, by Markov chains are vast and are still expanding. The aim of this book is a discussion of the time-dependent behavior, called the transient behavior, of Markov chains. From the practical point of view, when modeling a stochastic system by a Markov chain, there are many instances in which time-limiting results such as stationary distributions have no meaning. Or, even when the stationary distribution is of some importance, it is often dangerous to use the stationary result alone without knowing the transient behavior of the Markov chain. Not many books have paid much attention to this topic, despite its obvious importance.
Markov processes for stochastic modeling
Markov processes for stochastic modeling - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Markov processes for stochastic modeling write by Oliver C. Ibe. This book was released on 2013. Markov processes for stochastic modeling available in PDF, EPUB and Kindle.
An Introduction to Stochastic Modeling
An Introduction to Stochastic Modeling - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook An Introduction to Stochastic Modeling write by Howard M. Taylor. This book was released on 2014-05-10. An Introduction to Stochastic Modeling available in PDF, EPUB and Kindle. An Introduction to Stochastic Modeling provides information pertinent to the standard concepts and methods of stochastic modeling. This book presents the rich diversity of applications of stochastic processes in the sciences. Organized into nine chapters, this book begins with an overview of diverse types of stochastic models, which predicts a set of possible outcomes weighed by their likelihoods or probabilities. This text then provides exercises in the applications of simple stochastic analysis to appropriate problems. Other chapters consider the study of general functions of independent, identically distributed, nonnegative random variables representing the successive intervals between renewals. This book discusses as well the numerous examples of Markov branching processes that arise naturally in various scientific disciplines. The final chapter deals with queueing models, which aid the design process by predicting system performance. This book is a valuable resource for students of engineering and management science. Engineers will also find this book useful.
Cycle Representations of Markov Processes
Cycle Representations of Markov Processes - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Cycle Representations of Markov Processes write by Sophia L. Kalpazidou. This book was released on 2013-06-29. Cycle Representations of Markov Processes available in PDF, EPUB and Kindle. This book provides new insight into Markovian dependence via the cycle decompositions. It presents a systematic account of a class of stochastic processes known as cycle (or circuit) processes - so-called because they may be defined by directed cycles. An important application of this approach is the insight it provides to electrical networks and the duality principle of networks. This expanded second edition adds new advances, which reveal wide-ranging interpretations of cycle representations such as homologic decompositions, orthogonality equations, Fourier series, semigroup equations, and disintegration of measures. The text includes chapter summaries as well as a number of detailed illustrations.
