Finite Markov chains and algorithmic applications

Includes bibliographical references (p. 108-112) and index.

Basics of probability theory
Markov chains
Computer simulation of Markov chains
Irreducible and aperiodic Markov chains
Stationary distributions
Reversible Markov chains
Markov chain Monte Carlo
Fast convergence of MCMC algorithms
Approximate counting
Propp-Wilson algorithm
Sandwiching
Propp-Wilson with read-once randomness
Simulated annealing
Further reading

Based on a lecture course given at Chalmers University of Technology, this 2002 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