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

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 andengineering, 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, emphasising modelsrather than general constructions. Basic mathematical facts are supplied as and when they are needed andhistorical information is sprinkled throughout.

'In this book, one can find a well-balanced mix of clearly explained theory, several classical and interesting examples which complement and integrate the theory discussed, as well as thought-provoking quotations and humorous sentences which make reading this book very pleasant. … A particular feature is the presence of worked examples which are aimed at giving explicit guidance about how to make the transition form the theory to the practical issue of solving problems. The results presented are always worked out in detail in a style that is never dry but full of hints about the properties and devices used as well as illustrative pictures to stimulate the reader's attention and insight. … The title might lead someone to think that this book is merely an introduction, however it is not only for the beginner.' Mathematical Reviews, '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, emphasising models rather than general constructions. Basic mathematical facts are supplied as and when they are needed and historical information is sprinkled throughout.' L'Enseignement Mathématique, 'I enjoyed reading this book a great deal … it is a great way to flesh out the general theory with concrete examples and applications.' MAA Reviews

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).