Markov Chain Process (Theory and Cases) PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Markov Chain Process (Theory and Cases) PDF full book. Access full book title Markov Chain Process (Theory and Cases) by Carlos Polanco. Download full books in PDF and EPUB format.
Author: Carlos Polanco Publisher: Bentham Science Publishers ISBN: 9815080482 Category : Mathematics Languages : en Pages : 203
Book Description
Markov Chain Process: Theory and Cases is designed for students of natural and formal sciences. It explains the fundamentals related to a stochastic process that satisfies the Markov property. It presents 10 structured chapters that provide a comprehensive insight into the complexity of this subject by presenting many examples and case studies that will help readers to deepen their acquired knowledge and relate learned theory to practice. This book is divided into four parts. The first part thoroughly examines the definitions of probability, independent events, mutually (and not mutually) exclusive events, conditional probability, and Bayes’ theorem, which are essential elements in Markov’s theory. The second part examines the elements of probability vectors, stochastic matrices, regular stochastic matrices, and fixed points. The third part presents multiple cases in various disciplines: Predictive computational science, Urban complex systems, Computational finance, Computational biology, Complex systems theory, and Computational Science in Engineering. The last part introduces learners to Fortran 90 programs and Linux scripts. To make the comprehension of Markov Chain concepts easier, all the examples, exercises, and case studies presented in this book are completely solved and given in a separate section. This book serves as a textbook (either primary or auxiliary) for students required to understand Markov Chains in their courses, and as a reference book for researchers who want to learn about methods that involve Markov Processes.
Author: Carlos Polanco Publisher: Bentham Science Publishers ISBN: 9815080482 Category : Mathematics Languages : en Pages : 203
Book Description
Markov Chain Process: Theory and Cases is designed for students of natural and formal sciences. It explains the fundamentals related to a stochastic process that satisfies the Markov property. It presents 10 structured chapters that provide a comprehensive insight into the complexity of this subject by presenting many examples and case studies that will help readers to deepen their acquired knowledge and relate learned theory to practice. This book is divided into four parts. The first part thoroughly examines the definitions of probability, independent events, mutually (and not mutually) exclusive events, conditional probability, and Bayes’ theorem, which are essential elements in Markov’s theory. The second part examines the elements of probability vectors, stochastic matrices, regular stochastic matrices, and fixed points. The third part presents multiple cases in various disciplines: Predictive computational science, Urban complex systems, Computational finance, Computational biology, Complex systems theory, and Computational Science in Engineering. The last part introduces learners to Fortran 90 programs and Linux scripts. To make the comprehension of Markov Chain concepts easier, all the examples, exercises, and case studies presented in this book are completely solved and given in a separate section. This book serves as a textbook (either primary or auxiliary) for students required to understand Markov Chains in their courses, and as a reference book for researchers who want to learn about methods that involve Markov Processes.
Author: Paul A. Gagniuc Publisher: John Wiley & Sons ISBN: 1119387558 Category : Mathematics Languages : en Pages : 252
Book Description
A fascinating and instructive guide to Markov chains for experienced users and newcomers alike This unique guide to Markov chains approaches the subject along the four convergent lines of mathematics, implementation, simulation, and experimentation. It introduces readers to the art of stochastic modeling, shows how to design computer implementations, and provides extensive worked examples with case studies. Markov Chains: From Theory to Implementation and Experimentation begins with a general introduction to the history of probability theory in which the author uses quantifiable examples to illustrate how probability theory arrived at the concept of discrete-time and the Markov model from experiments involving independent variables. An introduction to simple stochastic matrices and transition probabilities is followed by a simulation of a two-state Markov chain. The notion of steady state is explored in connection with the long-run distribution behavior of the Markov chain. Predictions based on Markov chains with more than two states are examined, followed by a discussion of the notion of absorbing Markov chains. Also covered in detail are topics relating to the average time spent in a state, various chain configurations, and n-state Markov chain simulations used for verifying experiments involving various diagram configurations. • Fascinating historical notes shed light on the key ideas that led to the development of the Markov model and its variants • Various configurations of Markov Chains and their limitations are explored at length • Numerous examples—from basic to complex—are presented in a comparative manner using a variety of color graphics • All algorithms presented can be analyzed in either Visual Basic, Java Script, or PHP • Designed to be useful to professional statisticians as well as readers without extensive knowledge of probability theory Covering both the theory underlying the Markov model and an array of Markov chain implementations, within a common conceptual framework, Markov Chains: From Theory to Implementation and Experimentation is a stimulating introduction to and a valuable reference for those wishing to deepen their understanding of this extremely valuable statistical tool. Paul A. Gagniuc, PhD, is Associate Professor at Polytechnic University of Bucharest, Romania. He obtained his MS and his PhD in genetics at the University of Bucharest. Dr. Gagniuc’s work has been published in numerous high profile scientific journals, ranging from the Public Library of Science to BioMed Central and Nature journals. He is the recipient of several awards for exceptional scientific results and a highly active figure in the review process for different scientific areas.
Author: Marius Iosifescu Publisher: Courier Corporation ISBN: 0486150585 Category : Mathematics Languages : en Pages : 305
Book Description
A self-contained treatment of finite Markov chains and processes, this text covers both theory and applications. Author Marius Iosifescu, vice president of the Romanian Academy and director of its Center for Mathematical Statistics, begins with a review of relevant aspects of probability theory and linear algebra. Experienced readers may start with the second chapter, a treatment of fundamental concepts of homogeneous finite Markov chain theory that offers examples of applicable models. The text advances to studies of two basic types of homogeneous finite Markov chains: absorbing and ergodic chains. A complete study of the general properties of homogeneous chains follows. Succeeding chapters examine the fundamental role of homogeneous infinite Markov chains in mathematical modeling employed in the fields of psychology and genetics; the basics of nonhomogeneous finite Markov chain theory; and a study of Markovian dependence in continuous time, which constitutes an elementary introduction to the study of continuous parameter stochastic processes.
Author: Xianping Guo Publisher: Springer Science & Business Media ISBN: 3642025471 Category : Mathematics Languages : en Pages : 240
Book Description
Continuous-time Markov decision processes (MDPs), also known as controlled Markov chains, are used for modeling decision-making problems that arise in operations research (for instance, inventory, manufacturing, and queueing systems), computer science, communications engineering, control of populations (such as fisheries and epidemics), and management science, among many other fields. This volume provides a unified, systematic, self-contained presentation of recent developments on the theory and applications of continuous-time MDPs. The MDPs in this volume include most of the cases that arise in applications, because they allow unbounded transition and reward/cost rates. Much of the material appears for the first time in book form.
Author: J. R. Norris Publisher: Cambridge University Press ISBN: 1107393477 Category : Mathematics Languages : en Pages : 260
Book Description
Markov chains are central to the understanding of random processes. This is not only because they pervade the applications of random processes, but also because one can calculate explicitly many quantities of interest. This textbook, aimed at advanced undergraduate or MSc students with some background in basic probability theory, focuses on Markov chains and quickly develops a coherent and rigorous theory whilst showing also how actually to apply it. Both discrete-time and continuous-time chains are studied. A distinguishing feature is an introduction to more advanced topics such as martingales and potentials in the established context of Markov chains. There are applications to simulation, economics, optimal control, genetics, queues and many other topics, and exercises and examples drawn both from theory and practice. It will therefore be an ideal text either for elementary courses on random processes or those that are more oriented towards applications.
Author: Kai Lai Chung Publisher: Springer ISBN: 3642496865 Category : Mathematics Languages : en Pages : 287
Book Description
The theory of Markov chains, although a special case of Markov processes, is here developed for its own sake and presented on its own merits. In general, the hypothesis of a denumerable state space, which is the defining hypothesis of what we call a "chain" here, generates more clear-cut questions and demands more precise and definitive an swers. For example, the principal limit theorem (§§ 1. 6, II. 10), still the object of research for general Markov processes, is here in its neat final form; and the strong Markov property (§ 11. 9) is here always applicable. While probability theory has advanced far enough that a degree of sophistication is needed even in the limited context of this book, it is still possible here to keep the proportion of definitions to theorems relatively low. . From the standpoint of the general theory of stochastic processes, a continuous parameter Markov chain appears to be the first essentially discontinuous process that has been studied in some detail. It is common that the sample functions of such a chain have discontinuities worse than jumps, and these baser discontinuities play a central role in the theory, of which the mystery remains to be completely unraveled. In this connection the basic concepts of separability and measurability, which are usually applied only at an early stage of the discussion to establish a certain smoothness of the sample functions, are here applied constantly as indispensable tools.
Author: Zhenting Hou Publisher: Springer Science & Business Media ISBN: 146130265X Category : Mathematics Languages : en Pages : 501
Book Description
The general theory of stochastic processes and the more specialized theory of Markov processes evolved enormously in the second half of the last century. In parallel, the theory of controlled Markov chains (or Markov decision processes) was being pioneered by control engineers and operations researchers. Researchers in Markov processes and controlled Markov chains have been, for a long time, aware of the synergies between these two subject areas. However, this may be the first volume dedicated to highlighting these synergies and, almost certainly, it is the first volume that emphasizes the contributions of the vibrant and growing Chinese school of probability. The chapters that appear in this book reflect both the maturity and the vitality of modern day Markov processes and controlled Markov chains. They also will provide an opportunity to trace the connections that have emerged between the work done by members of the Chinese school of probability and the work done by the European, US, Central and South American and Asian scholars.
Author: Wai-Ki Ching Publisher: Springer Science & Business Media ISBN: 038729337X Category : Mathematics Languages : en Pages : 212
Book Description
Markov chains are a particularly powerful and widely used tool for analyzing a variety of stochastic (probabilistic) systems over time. This monograph will present a series of Markov models, starting from the basic models and then building up to higher-order models. Included in the higher-order discussions are multivariate models, higher-order multivariate models, and higher-order hidden models. In each case, the focus is on the important kinds of applications that can be made with the class of models being considered in the current chapter. Special attention is given to numerical algorithms that can efficiently solve the models. Therefore, Markov Chains: Models, Algorithms and Applications outlines recent developments of Markov chain models for modeling queueing sequences, Internet, re-manufacturing systems, reverse logistics, inventory systems, bio-informatics, DNA sequences, genetic networks, data mining, and many other practical systems.
Author: Ehrhard Behrends Publisher: Vieweg+Teubner Verlag ISBN: 3322901572 Category : Mathematics Languages : en Pages : 237
Book Description
Besides the investigation of general chains the book contains chapters which are concerned with eigenvalue techniques, conductance, stopping times, the strong Markov property, couplings, strong uniform times, Markov chains on arbitrary finite groups (including a crash-course in harmonic analysis), random generation and counting, Markov random fields, Gibbs fields, the Metropolis sampler, and simulated annealing. With 170 exercises.
Author: Carlos Polanco
Author: Carlos Polanco
Author: Paul A. Gagniuc
Author: Marius Iosifescu
Author: Xianping Guo
Author: Kai Lai Chung
Author: J. R. Norris
Author: Kai Lai Chung
Author: Zhenting Hou
Author: Wai-Ki Ching
Author: Ehrhard Behrends