Introduction to Markov Chains - 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 Introduction to Markov Chains write by Ehrhard Behrends. This book was released on 2014-07-08. Introduction to Markov Chains available 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.
An Introduction to Markov Processes
An Introduction to 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 An Introduction to Markov Processes write by Daniel W. Stroock. This book was released on 2005-03-30. An Introduction to Markov Processes available 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
Probability and Random Processes for Electrical and Computer Engineers
Probability and Random Processes for Electrical and Computer Engineers - 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 Probability and Random Processes for Electrical and Computer Engineers write by John A. Gubner. This book was released on 2006-06-01. Probability and Random Processes for Electrical and Computer Engineers available in PDF, EPUB and Kindle. The theory of probability is a powerful tool that helps electrical and computer engineers to explain, model, analyze, and design the technology they develop. The text begins at the advanced undergraduate level, assuming only a modest knowledge of probability, and progresses through more complex topics mastered at graduate level. The first five chapters cover the basics of probability and both discrete and continuous random variables. The later chapters have a more specialized coverage, including random vectors, Gaussian random vectors, random processes, Markov Chains, and convergence. Describing tools and results that are used extensively in the field, this is more than a textbook; it is also a reference for researchers working in communications, signal processing, and computer network traffic analysis. With over 300 worked examples, some 800 homework problems, and sections for exam preparation, this is an essential companion for advanced undergraduate and graduate students. Further resources for this title, including solutions (for Instructors only), are available online at www.cambridge.org/9780521864701.
Introduction to the Numerical Solution of Markov Chains
Introduction to the Numerical Solution of Markov Chains - 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 Introduction to the Numerical Solution of Markov Chains write by William J. Stewart. This book was released on 1994-12-04. Introduction to the Numerical Solution of Markov Chains available in PDF, EPUB and Kindle. Markov Chains -- Direct Methods -- Iterative Methods -- Projection Methods -- Block Hessenberg Matrices -- Decompositional Methods -- LI-Cyclic Markov -- Chains -- Transient Solutions -- Stochastic Automata Networks -- Software.
Markov Chains
Markov Chains - 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 Chains write by Paul A. Gagniuc. This book was released on 2017-07-31. Markov Chains available 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.
