Case Studies in Bayesian Statistical Modelling and Analysis

Clair L. Alston, Kerrie L. Mengersen, Anthony N. Pettitt, Clair L. Alston, Kerrie L. Mengersen, Anthony N. Pettitt

• Publisher: Wiley
• Year: 2012
• Print ISBN: 9781119941828
• eBook ISBN: 9781118394328
• Edition: 1
• Topic: Mathematics
• Subtopic: Probability & Statistics
• Index: Mathematics

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

• Cover
• Wiley Series in Probability and Statistics
• Title Page
• Copyright
• Preface
• List of contributors
• Chapter 1: Introduction
• Chapter 2: Introduction to MCMC
• Chapter 3: Priors: Silent or active Partners of Bayesian Inference?
• Chapter 4: Bayesian Analysis of the Normal Linear Regression Model
• Chapter 5: Adapting ICU Mortality Models for Local Data: A Bayesian Approach
• Chapter 6: A Bayesian Regression Model with Variable Selection for Genome-Wide Association Studies
• Chapter 7: Bayesian Meta-Analysis
• Chapter 8: Bayesian Mixed Effects Models
• Chapter 9: Ordering of Hierarchies in Hierarchical Models: Bone Mineral Density Estimation
• Chapter 10: Bayesian Weibull Survival Model for Gene Expression Data
• Chapter 11: Bayesian Change Point Detection in Monitoring Clinical Outcomes
• Chapter 12: Bayesian Splines
• Chapter 13: Disease Mapping using Bayesian Hierarchical Models
• Chapter 14: Moisture, Crops and Salination: An Analysis of a Three-Dimensional Agricultural Data Set
• Chapter 15: A Bayesian Approach to Multivariate State Space Modelling: A Study of a Fama–French Asset-Pricing Model with Time-Varying Regressors
• Chapter 16: Bayesian Mixture Models: When the Thing you need to know is the Thing you cannot Measure
• Chapter 17: Latent Class Models in Medicine
• Chapter 18: Hidden Markov Models for Complex Stochastic Processes: A Case Study in Electrophysiology
• Chapter 19: Bayesian Classification and Regression Trees
• Chapter 20: Tangled Webs: Using Bayesian Networks in the Fight Against Infection
• Chapter 21: Implementing Adaptive Dose Finding Studies using Sequential Monte Carlo
• Chapter 22: Likelihood-Free Inference for Transmission Rates of Nosocomial Pathogens
• Chapter 23: Variational Bayesian Inference for Mixture Models
• Chapter 24: Issues in Designing Hybrid Algorithms
• Chapter 25: A Python Package for Bayesian Estimation using Markov Chain Monte Carlo
• Index
• Wiley Series in Probability and Statistics