Login

Liang, Faming

Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples

Liang, Faming - Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples, ebook

96,70€

Ebook, ePUB with Adobe DRM
ISBN: 9781119956808
DRM Restrictions

Printing112 pages with an additional page accrued every 7 hours, capped at 112 pages
Copy to clipboard19 excerpts

This book provides comprehensive coverage of simulation of complex systems using Monte Carlo methods.

Developing algorithms that are immune to the local trap problem has long been considered as the most important topic in MCMC research. Various advanced MCMC algorithms which address this problem have been developed include, the modified Gibbs sampler, the methods based on auxiliary variables and the methods making use of past samples. The focus of this book is on the algorithms that make use of past samples.

This book includes the multicanonical algorithm, dynamic weighting, dynamically weighted importance sampling, the Wang-Landau algorithm, equal energy sampler, stochastic approximation Monte Carlo, adaptive MCMC algorithms, conjugate gradient Monte Carlo, adaptive direction sampling, the sampling Metropolis-Hasting algorithm and the multiplica sampler.

Author(s)
 
 
Publisher
John Wiley and Sons, Inc.
Publication year
2010
Language
en
Edition
1
Series
Wiley Series in Computational Statistics
Page amount
384 pages
Category
Natural Sciences
Format
Ebook
eISBN (ePUB)
9781119956808
Printed ISBN
9780470748268

Similar titles