Finite Markov Chains and Algorithmic Applications

Olle Häggström

114 pages, parution le 24/05/2002

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Résumé

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.

Contents

1. Basics of probability theory
2. Markov chains
3. Computer simulation of Markov chains
4. Irreducible and aperiodic Markov chains
5. Stationary distributions
6. Reversible Markov chains
7. Markov chain Monte Carlo
8. Fast convergence of MCMC algorithms
9. Approximate counting
10. Propp-Wilson algorithm
11. Sandwiching
12. Propp-Wilson with read once randomness
13. Simulated annealing
14. Further reading

Caractéristiques techniques

	PAPIER
Éditeur(s)	Cambridge University Press
Auteur(s)	Olle Häggström
Parution	24/05/2002
Nb. de pages	114
Format	15,5 x 22,5
Couverture	Broché
Poids	191g
Intérieur	Noir et Blanc
EAN13	9780521890014
ISBN13	978-0-521-89001-4