|
|
Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples (Wiley Series in Computational Statistics)
Faming Liang
Hardcover. Wiley 2010-09-14.
ISBN 9780470748268
|
|
|
Hitta bokens lägsta pris
|
Förlagets beskrivning
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
Fler böcker av Faming Liang
Liknande böcker
Recensioner
Den här boken har tyvärr inte några recensioner ännu. Om du redan läst boken, skriv en recension!
Recensera boken
Skriv en recension och dela dina åsikter med andra. Försök att fokusera på bokens innehåll. Läs våra instruktioner för mer information.
Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples
Bokrecensioner » Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples (Wiley Series in Computational Statistics)
|
|
|
|
|
|
|