The Oxford Handbook of Applied Bayesian Analysis [Hardcover]

Bayesian analysis has developed rapidly in applications in the last two decades and research in Bayesian methods remains dynamic and fast-growing. Dramatic advances in modelling concepts and computational technologies now enable routine application of Bayesian analysis using increasingly realistic stochastic models, and this drives the adoption of Bayesian approaches in many areas of science, technology, commerce, and industry.

This Handbook explores contemporary Bayesian analysis across a variety of application areas. Chapters written by leading exponents of applied Bayesian analysis showcase the scientific ease and natural application of Bayesian modelling, and present solutions to real, engaging, societally important and demanding problems. The chapters are grouped into five general areas: Biomedical & Health Sciences; Industry, Economics & Finance; Environment & Ecology; Policy, Political & Social Sciences; and Natural & Engineering Sciences, and Appendix material in each touches on key concepts, models, and techniques of the chapter that are also of broader pedagogic and applied interest.

Preface
Part I - Biomedical & Health Sciences
1. Flexible Bayes Regression of Epidemiologic Data,David Dunson
2. Bayesian Modelling for Matching and Alignment of Biomolecules,Peter Green, Kanti Mardia, Vysaul Nyirongo & Yann Ruffieux
3. Bayesian Approaches to Aspects of the Vioxx Trials: Non-ignorable Dropout and Sequential Meta-Analysis,Jerry Cheng & David Madigan
4. Sensitivity Analysis in Microbial Risk Assessment: Vero-cytotoxigenicE.coliO157 in Farm-Pasteurised Milk,Jeremy Oakley & Helen Clough
5. Mapping Malaria in the Amazon Rain Forest: a Spatio-Temporal Mixture Model,Alexandra Schmidt, Jennifer Hoeting, Jo??o Batista Pereira & Pedro Paulo Vieira
6. Trans-Study Projection of Genomic Biomarkers in Analysis of Oncogene Deregulation and Breast Cancer,Dan Merl, Joslƒ.