Description 
1 online resource 
Contents 
From observation to simulation  Building the stochastic matrix  Predictions by using 2state Markov chains  Predictions by using Nstate Markov chains  Absorbing Markov chains  The average time spent in each state  Discussions on different configurations of chains  The simulation of an Nstate Markov chain 
Summary 
'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 discretetime 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 twostate Markov chain. The notion of steady state is explored in connection with the longrun 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 nstate 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 
Bibliography 
Includes bibliographical references and index 
Notes 
Print version record and CIP data provided by publisher; resource not viewed 
Subject 
Markov processes


MATHEMATICS  Applied.


MATHEMATICS  Probability & Statistics  General.


Markov processes.

Form 
Electronic book

LC no. 
2017018061 
ISBN 
1119387574 (electronic bk.) 

1119387590 (electronic bk.) 

9781119387572 (electronic bk.) 

9781119387596 (electronic bk.) 

(cloth) 
