OXFORD UNIVERSITY PRESS

The Oxford Handbook of Applied Bayesian Analysis

ISBN : 9780198703174

Price(incl.tax): 
¥13,299
Author: 
Anthony O' Hagan; Mike West
Pages
928 Pages
Format
Paperback
Size
172 x 245 mm
Pub date
Oct 2013
Series
Oxford Handbooks in Mathematics
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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.

Index: 

Preface
PART I - BIOMEDICAL & HEALTH SCIENCES
1. Flexible Bayes Regression of Epidemiologic Data
2. Bayesian Modelling for Matching and Alignment of Biomolecules
3. Bayesian Approaches to Aspects of the Vioxx Trials: Non-ignorable Dropout and Sequential Meta-Analysis
4. Sensitivity Analysis in Microbial Risk Assessment: Vero-cytotoxigenic E.coli O157 in Farm-Pasteurised Milk
5. Mapping Malaria in the Amazon Rain Forest: a Spatio-Temporal Mixture Model
6. Trans-Study Projection of Genomic Biomarkers in Analysis of Oncogene Deregulation and Breast Cancer
7. Linking Systems Biology Models to Data: a Stochastic Kinetic Model of p53 Oscillations
PART II - INDUSTRY, ECONOMICS & FINANCE
8. Bayesian Analysis and Decisions in Nuclear Power Plant Maintenance
9. Bayes Linear Uncertainty Analysis for Oil Reservoirs Based on Multiscale Computer Experiments
10. Bayesian Modelling of Train Doors Reliability
11. Analysis of Economic Data With Multiscale Spatio-temporal Models
12. Extracting S&P500 and NASDAQ Volatility: The Credit Crisis of 2007-2008
13. Futures Markets, Bayesian Forecasting, and Risk Modeling
14. The New Macroeconometrics: A Bayesian Approach
PART III - ENVIRONMENT & ECOLOGY
15. Assessing The Probability of Rare Climate Events
16. Models for Demography of Plant Populations
17. Combining Monitoring Data and Computer Model Output in Assessing Environmental Exposure
18. Indirect Elicitation From Ecological Experts: From Methods and Software to Habitat Modelling and Rock-Wallabies
19. Characterizing the Uncertainty of Climate Change Projections Using Hierarchical Models
PART IV - POLICY, POLITICAL & SOCIAL SCIENCES
20. Volatility in Prediction Markets: A Measure of Information Flow in Political Campaigns
21. Paternity Testing Allowing for Uncertain Mutation Rates
22. Bayesian Analysis in Item Response Theory Applied to a Large-scale Educational Assessment
23. Sequential Multi-location Auditing and the New York Food Stamps Program
24. Bayesian Causal Inference: Approaches to Estimating the Effect of Treating Hospital Type on Cancer Survival in Sweden Using Principal Stratification
PART V - NATURAL & ENGINEERING SCIENCES
25. Bayesian Statistical Methods for Audio and Music Processing
26. Combining Simulations and Physical Observations to Estimate Cosmological Parameters
27. Probabilistic Grammars and Hierarchical Dirichlet Processes
28. Designing and Analyzing a Circuit Device Experiment Using Treed Gaussian Processes
29. Multi-state Models for Mental Fatigue
Index

About the author: 

Anthony O'Hagan is internationally recognized for his research in the methodology and applications of Bayesian statistics. Following BSc and PhD degrees from the University of London, he taught at the Universities of Dundee and Warwick before becoming a full professor at the University of Nottingham and then the University of Sheffield. He has also spent two years working in the electricity industry. He has substantial applied expertise from applications in many fields, including engineering, health, environmental science and finance. ; Mike West is an international research and educational leader in statistical science whose areas of expertise span a range of areas in Bayesian statistical modelling and computational statistics, and inter-disciplinary applications in science, biomedicine, finance and other areas. West was a faculty member at the leading Bayesian centre at Warwick University UK in the 1980s, and led the development of one of the main centres worldwide - at Duke University -- during the 1990s and into the Bayesian 21st century. As distinguished professor of statistical science at Duke University, West is broadly engaged in national and international professional activities, his research continues to emphasise Bayesian methodology development and applications of complex stochastic modelling, while his major professional focus remains the engagement and mentoring of future statistical scientists.

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