Esta web utiliza cookies propias y de terceros que nos permiten optimizar tu experiencia en el sitio web, evaluar su rendimiento, generar estadísticas de uso y mejorar y añadir nuevas funcionalidades. Mediante el análisis de tus hábitos de navegación podemos mostrar contenidos más relevantes y medir las interacciones con la web.
Puede obtener más información aquí.

Avançat
Articles
Portada

Monte Carlo Methods in Bayesian Computation

Shao, Qi Man
SPRINGER
10 / 2012
9781461270744
Anglès
Springer Series in Statistics
Probabilidad y estadistica
Software de matematicas y estadisticas
Ingles

Sinopsi

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.

PVP
103,99€