Kerrie L. Mengersen

Clair L. Alston

Anthony N. Pettitt

Provides an accessible foundation to Bayesian analysis usingreal world models This book aims to present an introduction to Bayesian modellingand computation, by considering real case studies drawn fromdiverse fields spanning ecology, health, genetics and finance. Eachchapter comprises a description of the problem, the correspondingmodel, the computational method, results and inferences as well asthe issues that arise in the implementation of theseapproaches. Case Studies in Bayesian Statistical Modelling andAnalysis: Illustrates how to do Bayesian analysis in a clear and concisemanner using real-world problems. Each chapter focuses on a real-world problem and describes theway in which the problem may be analysed using Bayesianmethods. Features approaches that can be used in a wide area ofapplication, such as, health, the environment, genetics,information science, medicine, biology, industry and remotesensing. Case Studies in Bayesian Statistical Modelling andAnalysis is aimed at statisticians, researchers andpractitioners who have some expertise in statistical modelling andanalysis, and some understanding of the basics of Bayesianstatistics, but little experience in its application. Graduatestudents of statistics and biostatistics will also find this bookbeneficial.

Case Studies in Bayesian Statistical Modelling and Analysis

Michael Whitby

Anthony Morton

Geoffrey Playford

Hospitals monitoring is becoming more complex and is increasingboth because staff want their data analysed and because ofincreasing mandated surveillance. This book provides a suiteof functions in R, enabling scientists and data analysts working ininfection management and quality improvement departments inhospitals, to analyse their often non-independent data which isfrequently in the form of trended, over-dispersed and sometimesauto-correlated time series; this is often difficult to analyseusing standard office software. This book provides much-needed guidance on data analysis using Rfor the growing number of scientists in hospital departments whoare responsible for producing reports, and who may have limitedstatistical expertise. This book explores data analysis using R and is aimed atscientists in hospital departments who are responsible forproducing reports, and who are involved in improving safety.Professionals working in the healthcare quality and safetycommunity will also find this book of interest Statistical Methods for Hospital Monitoring with R: Provides functions to perform quality improvement and infectionmanagement data analysis. Explores the characteristics of complex systems, such asself-organisation and emergent behaviour, along with theirimplications for such activities as root-cause analysis and thePareto principle that seek few key causes of adverse events. Provides a summary of key non-statistical aspects of hospitalsafety and easy to use functions. Provides R scripts in an accompanying web site enablinganalyses to be performed by the reader ahref="http://www.wiley.com/go/hospital_monitoring"http://www.wiley.com/go/hospital_monitoring/a Covers issues that will be of increasing importance in thefuture, such as, generalised additive models, and complex systems,networks and power laws.

Kerrie L. Mengersen

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