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. As discussed in the Preface, the chapters are intended to be introductory and it is openly acknowledged that there may be many other ways to address the case studies presented here. However, the intention is to provide the Bayesian beginner with a practical and accessible foundation on which to build their own Bayesian solutions to problems encountered in research and practice.
In the following, we first provide an overview of the chapters in the book and then present a list of texts for further reading. This book does not seek to teach the novice about Bayesian statistics per se, nor does it seek to cover the whole field. However, there is now a substantial literature on Bayesian theory, methodology, computation and application that can be used as support and extension. While we cannot hope to cover all of the relevant publications, we provide a selected review of texts now available on Bayesian statistics, in the hope that this will guide the reader to other reference material of interest.
In this section we give an overview of the chapters in this book. Given that the models are developed and described in the context ...