Provides an accessible foundation to Bayesian analysis using real world models
This 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.
Table of Contents
1 Introduction 1
2 Introduction to MCMC 17
3 Priors: Silent or active partners of Bayesian inference? 30
4 Bayesian analysis of the normal linear regression model 66
5 Adapting ICU mortality models for local data: A Bayesian approach 90
6 A Bayesian regression model with variable selection for genome-wide association studies 103
7 Bayesian meta-analysis 118
8 Bayesian mixed effects models 141
9 Ordering of hierarchies in hierarchical models: Bone mineral density estimation 159
10 BayesianWeibull survival model for gene expression data 171
11 Bayesian change point detection in monitoring clinical outcomes 186
12 Bayesian splines 197
13 Disease mapping using Bayesian hierarchical models 221
14 Moisture, crops and salination: An analysis of a three-dimensional agricultural data set 240
15 A Bayesian approach to multivariate state space modelling: A study of a Fama–French asset-pricing model with time-varying regressors 252
16 Bayesian mixture models: When the thing you need to know is the thing you cannot measure 267
17 Latent class models in medicine 287
18 Hidden Markov models for complex stochastic processes: A case study in electrophysiology 310
19 Bayesian classification and regression trees 330
20 Tangled webs: Using Bayesian networks in the fight against infection 348
21 Implementing adaptive dose finding studies using sequential Monte Carlo 361
22 Likelihood-free inference for transmission rates of nosocomial pathogens 374
23 Variational Bayesian inference for mixture models 388
24 Issues in designing hybrid algorithms 403
25 A Python package for Bayesian estimation using Markov chain Monte Carlo 421