Description:
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.
Review Quotes:
"As such, this book can serve as a handy reference for proficient statisticians and programmers." (The Quarterly Review of Biology, 1 October 2015)