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bruno sericola markov chains theory and applications

Bruno Sericola Markov Chains. Theory and Applications bruno sericola markov chains theory and applications

Bruno Sericola Marko...

Markov chains are a fundamental class of stochastic processes. They ar...

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Группа авторов Markov Processes and Applications bruno sericola markov chains theory and applications

Группа авторов Marko...

This well-written book provides a clear and accessible treatment of th...

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Paul Gagniuc A. Markov Chains. From Theory to Implementation and Experimentation bruno sericola markov chains theory and applications

Paul Gagniuc A. Mark...

A fascinating and instructive guide to Markov chains for experienced u...

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Poggiolini Laura A First Course in Probability and Markov Chains bruno sericola markov chains theory and applications

Poggiolini Laura A F...

Provides an introduction to basic structures of probability with a vie...

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Gunter Bolch Queueing Networks and Markov Chains bruno sericola markov chains theory and applications

Gunter Bolch Queuein...

Critically acclaimed text for computer performance analysis–now in its...

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Carl Graham Markov Chains. Analytic and Monte Carlo Computations bruno sericola markov chains theory and applications

Carl Graham Markov C...

Markov Chains: Analytic and Monte Carlo Computations introduces the ma...

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Ionut Florescu Probability and Stochastic Processes bruno sericola markov chains theory and applications

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A comprehensive and accessible presentation of probability and stochas...

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Moyal Pascal Stochastic Modeling and Analysis of Telecom Networks bruno sericola markov chains theory and applications

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This book addresses the stochastic modeling of telecommunication netwo...

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Marvin Rausand System Reliability Theory bruno sericola markov chains theory and applications

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A comprehensive introduction to reliability analysis. The first sectio...

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Группа авторов Probability and Statistics with Reliability, Queuing, and Computer Science Applications bruno sericola markov chains theory and applications

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An accessible introduction to probability, stochastic processes, and s...

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Цена: 17500.45 RUR

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Yuliya Mishura Theory and Statistical Applications of Stochastic Processes bruno sericola markov chains theory and applications

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This book is concerned with the theory of stochastic processes and the...

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John Kalbfleisch D. The Statistical Analysis of Failure Time Data bruno sericola markov chains theory and applications

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Contains additional discussion and examples on left truncation as well...

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Цена: 17866.95 RUR

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Serguei Primak Stochastic Methods and their Applications to Communications bruno sericola markov chains theory and applications

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Stochastic Methods & their Applications to Communications presents a v...

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Цена: 21440.34 RUR

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Robert Dobrow P. Probability. With Applications and R bruno sericola markov chains theory and applications

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An introduction to probability at the undergraduate level Chance and r...

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Thomas Augustin Introduction to Imprecise Probabilities bruno sericola markov chains theory and applications

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In recent years, the theory has become widely accepted and has been fu...

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Jeonghoon Mo Performance Modeling of Communication Networks with Markov Chains bruno sericola markov chains theory and applications

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Jeonghoon Mo Performance Modeling of Communication Networks with Marko...

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Для информации:

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.