Monte carlo strategies in scientific computing

Jun S. Liu - Collection Springer Series In Statistics

346 pages, parution le 15/07/2001

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Résumé

A large number of scientists and engineers employ Monte Carlo simulation and related global optimization techniques (such as simulated annealing) as an essential tool in their work. For such scientists, there is a need to keep up to date with several recent advances in Monte Carlo methodologies such as cluster methods, data- augmentation, simulated tempering and other auxiliary variable methods. There is also a trend in moving towards a population-based approach. All these advances in one way or another were motivated by the need to sample from very complex distribution for which traditional methods would tend to be trapped in local energy minima. It is our aim to provide a self-contained and up to date treatment of the Monte Carlo method to this audience.

Sommaire

Introduction and Preliminaries
Sequential Importance Sampling
Metropolis-Hastings Algorithms
The Gibbs Sampler
Partial Resampling and Generalized Gibbs
Cross Dimensions and Energy Barriers
Tempering Methods
Evolutionary Approach in MCMC
Some Applications of Monte Carlo Methods
Other Related Topics

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Caractéristiques techniques

	PAPIER
Éditeur(s)	Springer
Auteur(s)	Jun S. Liu
Collection	Springer Series In Statistics
Parution	15/07/2001
Nb. de pages	346
Format	16 x 24
Couverture	Relié
Poids	620g
Intérieur	Noir et Blanc
EAN13	9780387952307
ISBN13	978-0-387-95230-7