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Markov Chains - From Theory to Implementation and Experimentation

Autor: Paul A. Gagniuc

Jazyk:

Vazba: Pevná

Vydavatel: John Wiley and Sons Ltd

Dostupnost: Skladem u dodavatele v malém množství

Odesíláme za 11-15 dnů

2 924 Kč

A fascinating and instructive guide to Markov chains for experienced users and newcomers alike This...

Informace o knize

Autor

Paul A. Gagniuc

Jazyk

Angličtina

Vazba

Kniha - Pevná

Vydáno

2017

Stránek

256

EAN

9781119387558

ISBN

9781119387558

Enbook ID

15735041

Vydavatel

John Wiley and Sons Ltd

Hmotnost

558

Rozměry

158 x 238 x 18

Kompletní popis

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.

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