Book contents
- Frontmatter
- Contents
- Preface
- 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 The Propp–Wilson algorithm
- 11 Sandwiching
- 12 Propp–Wilson with read-once randomness
- 13 Simulated annealing
- 14 Further reading
- References
- Index
11 - Sandwiching
Published online by Cambridge University Press: 29 March 2010
- Frontmatter
- Contents
- Preface
- 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 The Propp–Wilson algorithm
- 11 Sandwiching
- 12 Propp–Wilson with read-once randomness
- 13 Simulated annealing
- 14 Further reading
- References
- Index
Summary
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- Type
- Chapter
- Information
- Finite Markov Chains and Algorithmic Applications , pp. 84 - 92Publisher: Cambridge University PressPrint publication year: 2002