Finite Markov Chains and Algorithmic Applications

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Format: Paperback
Pub. Date: 2002-06-10
Publisher(s): Cambridge University Press
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Summary

Based on a lecture course given at Chalmers University of Technology, this book is ideal for advanced undergraduate or beginning graduate students. The author first develops the necessary background in probability theory and Markov chains before applying it to study a range of randomized algorithms with important applications in optimization and other problems in computing. Amongst the algorithms covered are the Markov chain Monte Carlo method, simulated annealing, and the recent Propp-Wilson algorithm. This book will appeal not only to mathematicians, but also to students of statistics and computer science. The subject matter is introduced in a clear and concise fashion and the numerous exercises included will help students to deepen their understanding.

Table of Contents

Preface vii
Basics of probability theory
1(7)
Markov chains
8(9)
Computer simulation of Markov chains
17(6)
Irreducible and aperiodic Markov chains
23(5)
Stationary distributions
28(11)
Reversible Markov chains
39(6)
Markov chain Monte Carlo
45(9)
Fast convergence of MCMC algorithms
54(10)
Approximate counting
64(12)
The Propp--Wilson algorithm
76(8)
Sandwiching
84(9)
Propp--Wilson with read-once randomness
93(6)
Simulated annealing
99(9)
Further reading
108(2)
References 110(3)
Index 113

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