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,...
Sampling from the posterior distribution and computing posterior quanti- ties of interest using Markov chain Monte Carlo (MCMC) samples are two major ...
As research into this subject diversifies, keeping up to date with the frontiers of the discipline becomes increasingly difficult. This book covers most of the current research challenges and opportunities, including Bayesian inference and nonparametric Bayes.
As research into this subject diversifies, keeping up to date with the frontiers of the discipline becomes increasingly difficult. This book covers...
Survival analysis arises in many fields of study including medicine, biology, engineering, public health, epidemiology, and economics. This book provides a comprehensive treatment of Bayesian survival analysis. Several topics are addressed, including parametric models, semiparametric models based on prior processes, proportional and non-proportional hazards models, frailty models, cure rate models, model selection and comparison, joint models for longitudinal and survival data, models with time varying covariates, missing covariate data, design and monitoring of clinical trials, accelerated...
Survival analysis arises in many fields of study including medicine, biology, engineering, public health, epidemiology, and economics. This book provi...
Offering a rich diversity of models, Bayesian phylogenetics allows evolutionary biologists, systematists, ecologists, and epidemiologists to obtain answers to very detailed phylogenetic questions. Suitable for graduate-level researchers in statistics and biology, Bayesian Phylogenetics: Methods, Algorithms, and Applications presents a snapshot of current trends in Bayesian phylogenetic research.
Encouraging interdisciplinary research, this book introduces state-of-the-art phylogenetics to the Bayesian statistical community and, likewise, presents...
Offering a rich diversity of models, Bayesian phylogenetics allows evolutionary biologists, systematists, ecologists, and epidemiologists to obtain...
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,...
Sampling from the posterior distribution and computing posterior quanti- ties of interest using Markov chain Monte Carlo (MCMC) samples are two major ...
