Wiley, 2012. — 598 p. — ISBN: 1119941822, 9781119941828This book aims to present an introduction to Bayesian modelling and computation, by considering real case studies drawn from diverse fields spanning ecology, health, genetics and finance. Each chapter comprises a description of the problem, the corresponding model, the computational method, results and inferences as well as the issues that arise in the implementation of these approaches.
Case Studies in Bayesian Statistical Modelling and Analysis:
Illustrates how to do Bayesian analysis in a clear and concise manner using real-world problems.
Each chapter focuses on a real-world problem and describes the way in which the problem may be analysed using Bayesian methods.
Features approaches that can be used in a wide area of application, such as, health, the environment, genetics, information science, medicine, biology, industry and remote sensing.Case Studies in Bayesian Statistical Modelling and Analysis is aimed at statisticians, researchers and practitioners who have some expertise in statistical modelling and analysis, and some understanding of the basics of Bayesian statistics, but little experience in its application. Graduate students of statistics and biostatistics will also find this book beneficial.Contents:
Preface
List of contributors
Introduction
Introduction to MCMC
Priors: Silent or active partners of Bayesian inference?
Bayesian analysis of the normal linear regression model
Adapting ICU mortality models for local data: A Bayesian approach
A Bayesian regression model with variable selection for genome-wide association studies
Bayesian meta-analysis
Bayesian mixed effects models
Ordering of hierarchies in hierarchical models: Bone mineral density estimation
BayesianWeibull survival model for gene expression data
Bayesian change point detection in monitoring clinical outcomes
Bayesian splines
Disease mapping using Bayesian hierarchical models
Moisture, crops and salination: An analysis of a three-dimensional agricultural data set
A Bayesian approach to multivariate state space modelling: A study of a Fama–French asset-pricing model with time-varying regressors
Bayesian mixture models: When the thing you need to know is the thing you cannot measure
Latent class models in medicine
Hidden Markov models for complex stochastic processes: A case study in electrophysiology
Bayesian classification and regression trees
Tangled webs: Using Bayesian networks in the fight against infection
Implementing adaptive dose finding studies using sequential Monte Carlo
Likelihood-free inference for transmission rates of nosocomial pathogens
Variational Bayesian inference for mixture models
Issues in designing hybrid algorithms
A Python package for Bayesian estimation using Markov chain Monte Carlo
Index
