An Introduction To Markov Processes PDF Download
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Author | : Daniel W. Stroock |
Publisher | : Springer Science & Business Media |
Total Pages | : 196 |
Release | : 2005-03-30 |
Genre | : Mathematics |
ISBN | : 9783540234517 |
Download An Introduction to Markov Processes Book in PDF, ePub and Kindle
Provides a more accessible introduction than other books on Markov processes by emphasizing the structure of the subject and avoiding sophisticated measure theory Leads the reader to a rigorous understanding of basic theory
Author | : Ehrhard Behrends |
Publisher | : Vieweg+Teubner Verlag |
Total Pages | : 237 |
Release | : 2014-07-08 |
Genre | : Mathematics |
ISBN | : 3322901572 |
Download Introduction to Markov Chains Book in PDF, ePub and Kindle
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 | : Thomas Milton Liggett |
Publisher | : American Mathematical Soc. |
Total Pages | : 290 |
Release | : 2010 |
Genre | : Mathematics |
ISBN | : 0821849492 |
Download Continuous Time Markov Processes Book in PDF, ePub and Kindle
Markov processes are among the most important stochastic processes for both theory and applications. This book develops the general theory of these processes, and applies this theory to various special examples.
Author | : Daniel W. Stroock |
Publisher | : |
Total Pages | : 208 |
Release | : 1984-08 |
Genre | : Large deviations |
ISBN | : 9781461385158 |
Download An Introduction to the Theory of Large Deviations Book in PDF, ePub and Kindle
Author | : Daniel T. Gillespie |
Publisher | : Gulf Professional Publishing |
Total Pages | : 600 |
Release | : 1992 |
Genre | : Mathematics |
ISBN | : 9780122839559 |
Download Markov Processes Book in PDF, ePub and Kindle
Markov process theory provides a mathematical framework for analyzing the elements of randomness that are involved in most real-world dynamical processes. This introductory text, which requires an understanding of ordinary calculus, develops the concepts and results of random variable theory.
Author | : Oliver Ibe |
Publisher | : Newnes |
Total Pages | : 515 |
Release | : 2013-05-22 |
Genre | : Mathematics |
ISBN | : 0124078397 |
Download Markov Processes for Stochastic Modeling Book 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.
Author | : Daniel W. Stroock |
Publisher | : Springer Science & Business Media |
Total Pages | : 213 |
Release | : 2013-10-28 |
Genre | : Mathematics |
ISBN | : 3642405231 |
Download An Introduction to Markov Processes Book in PDF, ePub and Kindle
This book provides a rigorous but elementary introduction to the theory of Markov Processes on a countable state space. It should be accessible to students with a solid undergraduate background in mathematics, including students from engineering, economics, physics, and biology. Topics covered are: Doeblin's theory, general ergodic properties, and continuous time processes. Applications are dispersed throughout the book. In addition, a whole chapter is devoted to reversible processes and the use of their associated Dirichlet forms to estimate the rate of convergence to equilibrium. These results are then applied to the analysis of the Metropolis (a.k.a simulated annealing) algorithm. The corrected and enlarged 2nd edition contains a new chapter in which the author develops computational methods for Markov chains on a finite state space. Most intriguing is the section with a new technique for computing stationary measures, which is applied to derivations of Wilson's algorithm and Kirchoff's formula for spanning trees in a connected graph.
Author | : Paul A. Gagniuc |
Publisher | : John Wiley & Sons |
Total Pages | : 252 |
Release | : 2017-07-31 |
Genre | : Mathematics |
ISBN | : 1119387558 |
Download Markov Chains Book in PDF, ePub and Kindle
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 |
Total Pages | : 305 |
Release | : 2014-07-01 |
Genre | : Mathematics |
ISBN | : 0486150585 |
Download Finite Markov Processes and Their Applications Book in PDF, ePub and Kindle
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 | : Etienne Pardoux |
Publisher | : John Wiley & Sons |
Total Pages | : 322 |
Release | : 2008-11-20 |
Genre | : Mathematics |
ISBN | : 0470721863 |
Download Markov Processes and Applications Book in PDF, ePub and Kindle
"This well-written book provides a clear and accessible treatment of the theory of discrete and continuous-time Markov chains, with an emphasis towards applications. The mathematical treatment is precise and rigorous without superfluous details, and the results are immediately illustrated in illuminating examples. This book will be extremely useful to anybody teaching a course on Markov processes." Jean-François Le Gall, Professor at Université de Paris-Orsay, France. Markov processes is the class of stochastic processes whose past and future are conditionally independent, given their present state. They constitute important models in many applied fields. After an introduction to the Monte Carlo method, this book describes discrete time Markov chains, the Poisson process and continuous time Markov chains. It also presents numerous applications including Markov Chain Monte Carlo, Simulated Annealing, Hidden Markov Models, Annotation and Alignment of Genomic sequences, Control and Filtering, Phylogenetic tree reconstruction and Queuing networks. The last chapter is an introduction to stochastic calculus and mathematical finance. Features include: The Monte Carlo method, discrete time Markov chains, the Poisson process and continuous time jump Markov processes. An introduction to diffusion processes, mathematical finance and stochastic calculus. Applications of Markov processes to various fields, ranging from mathematical biology, to financial engineering and computer science. Numerous exercises and problems with solutions to most of them