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Cambridge University Press
Probability and Statistics by Example, Volume 2: Markov Chains: A Primer in Random Processes and their Applications

Probability and Statistics by Example, Volume 2: Markov Chains: A Primer in Random Processes and their Applications

by Yuri Suhov, Mark Kelbert
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Probability and Statistics are as much about intuition and problem solving as they are about theorem proving. Because of this, students can find it very difficult to make a successful transition from lectures to examinations to practice, since the problems involved can vary so much in nature. Since the subject is critical in many modern applications such as mathematical finance, quantitative management, telecommunications, signal processing, bioinformatics, as well as traditional ones such as insurance, social science and engineering, the authors have rectified deficiencies in traditional lecture-based methods by collecting together a wealth of exercises with complete solutions, adapted to needs and skills of students. Following on from the success of Probability and Statistics by Example: Basic Probability and Statistics, the authors here concentrate on random processes, particularly Markov processes, emphasizing models rather than general constructions. Basic mathematical facts are supplied as and when they are needed and historical information is sprinkled throughout.

Product Details

ISBN-13: 9780521612340
Publisher: Cambridge University Press
Publication date: 11/30/2007
Edition description: New Edition
Pages: 504
Product dimensions: 6.85(w) x 9.72(h) x 6.85(d)

About the Author

Yuri Suhov is a Professor of Applied Probability in the Department of Pure Mathematics and Mathematical Statistics at the University of Cambridge.

Mark Kelbert is a Reader in Statistics in the Department of Mathematics at Swansea University. For many years he has also been associated with the Moscow Institute of Information Transmission Problems and the International Institute of Earthquake Prediction Theory and Mathematical Geophysics (Moscow).

Table of Contents

Preface; Introduction: Andrei Markov and his time; 1. Discrete-time Markov chains; 2. Continuous-time Markov chains: basic theory; 3. Statistics of discrete-time Markov chains; Afterword: Pearson, Maxwell and other famous Cambridge Wranglers of the past: some lessons to be learned; Bibliography; Appendix; Index.

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