9780387989358 - monte carlo methods in bayesian computation (springer series in statistics) von chen, ming-hui; shao, qi-man; ibrahim, joseph g. (3 Ergebnisse)

Sprache: Englisch
Verlag: Springer, 2000
- Hardcover
Anbieter: Books From California, Simi Valley, CA, USABooks From California
Verkäufer/-in kontaktierenVerkäufer/-in mit 4 SternenZustand: Gebraucht - Befriedigend
EUR 78,77
EUR 4,31 VersandVersand innerhalb von USAAnzahl: 1 verfügbar
hardcover. Zustand: Good. Ex-library copy, with the usual markings/stickers/stamping present. Otherwise book & it's text block show minimal shelf & handling wear, crisp and clean. Text/pictures are unmarked, binding intact and firm! A good reading copy.

Sprache: Englisch
Verlag: Springer, 2000
- Hardcover
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes KönigreichRia Christie Collections
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 116,59
EUR 14,00 VersandVersand von Vereinigtes Königreich nach USAAnzahl: Mehr als 20 verfügbar
Zustand: New. In.

Sprache: Englisch
Verlag: Springer, Springer, 2000
- Hardcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 114,36
EUR 63,87 VersandVersand von Deutschland nach USAAnzahl: 1 verfügbar
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Sampling from the posterior distribution and computing posterior quanti ties of interest using Markov chain Monte Carlo (MCMC) samples are two major challenges involved in advanced Bayesian computation. This book examines each of these issues in detail and…focuses heavily on comput ing various posterior quantities of interest from a given MCMC sample. Several topics are addressed, including techniques for MCMC sampling, Monte Carlo (MC) methods for estimation of posterior summaries, improv ing simulation accuracy, marginal posterior density estimation, estimation of normalizing constants, constrained parameter problems, Highest Poste rior Density (HPD) interval calculations, computation of posterior modes, and posterior computations for proportional hazards models and Dirichlet process models. Also extensive discussion is given for computations in volving model comparisons, including both nested and nonnested models. Marginal likelihood methods, ratios of normalizing constants, Bayes fac tors, the Savage-Dickey density ratio, Stochastic Search Variable Selection (SSVS), Bayesian Model Averaging (BMA), the reverse jump algorithm, and model adequacy using predictive and latent residual approaches are also discussed. The book presents an equal mixture of theory and real applications.