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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.
The Oxford Handbook of Applied Bayesian Analysis 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: David Dunson: Flexible Bayes Regression of Epidemiologic Data
2: Peter Green, Kanti Mardia, Vysaul Nyirongo & Yann Ruffieux: Bayesian Modelling for Matching and Alignment of Biomolecules
3: Jerry Cheng & David Madigan: Bayesian Approaches to Aspects of the Vioxx Trials: Non-ignorable Dropout and Sequential Meta-Analysis
4: Jeremy Oakley & Helen Clough: Sensitivity Analysis in Microbial Risk Assessment: Vero-cytotoxigenic E.coli O157 in Farm-Pasteurised Milk
5: Alexandra Schmidt, Jennifer Hoeting, João Batista Pereira & Pedro Paulo Vieira: Mapping Malaria in the Amazon Rain Forest: a Spatio-Temporal Mixture Model
6: Dan Merl, Joseph Lucas, Joseph Nevins, Haige Shenz & Mike West: Trans-Study Projection of Genomic Biomarkers in Analysis of Oncogene Deregulation and Breast Cancer
7: D. A. Henderson, R.J. Boys, C.J. Proctor & D.J. Wilkinson: Linking Systems Biology Models to Data: a Stochastic Kinetic Model of p53 Oscillations
Part II - Industry, Economics & Finance
8: Elmira Popova, David Morton, Paul Damien & Tim Hanson: Bayesian Analysis and Decisions in Nuclear Power Plant Maintenance
9: Jonathan Cumming & Michael Goldstein: Bayes Linear Uncertainty Analysis for Oil Reservoirs Based on Multiscale Computer Experiments
10: Antonio Pievatolo & Fabrizio Ruggeri: Bayesian Modelling of Train Doors Reliability
11: Marco Ferreira, Adelmo Bertoldey & Scott Holan: Analysis of Economic Data With Multiscale Spatio-temporal Models
12: Hedibert Lopes & Nicholas Polson: Extracting S&P500 and NASDAQ Volatility: The Credit Crisis of 2007-2008
13: José Mario Quintana, Carlos Carvalho, James Scott & Thomas Costigliola: Futures Markets, Bayesian Forecasting, and Risk Modeling
14: Jesús Fernández-Villaverde, Pablo Guerrón-Quintana & Juan Rubio-Ramírez: The New Macroeconometrics: A Bayesian Approach
Part III - Environment & Ecology
15: Peter Challenor, Doug McNeall & James Gattiker: Assessing The Probability of Rare Climate Events
16: James Clark, Dave Bell, Michael Dietze, Michelle Hersh, Ines Ibanez, Shannon LaDeau, Sean McMahon, Jessica Metcalf, Emily Moran, Luke Pangle & Mike Wolosin: Models for Demography of Plant Populations
17: Alan Gelfand & Sujit K. Sahu: Combining Monitoring Data and Computer Model Output in Assessing Environmental Exposure
18: Samantha Low Choy, Justine Murray, Allan James & Kerrie Mengersen: Indirect Elicitation From Ecological Experts: From Methods and Software to Habitat Modelling and Rock-Wallabies
19: Claudia Tebaldi & Richard Smith: Characterizing the Uncertainty of Climate Change Projections Using Hierarchical Models
Part IV - Policy, Political & Social Sciences
20: Carlos Carvalho & Jill Rickershauser: Volatility in Prediction Markets: A Measure of Information Flow in Political Campaigns
21: Philip Dawid, Julia Mortera & Paola Vicard: Paternity Testing Allowing for Uncertain Mutation Rates
22: Dani Gamerman, Tufi Soares & Flávio Gonçalves: Bayesian Analysis in Item Response Theory Applied to a Large-scale Educational Assessment
23: Karl Heiner, Marc Kennedy & Anthony O'Hagan: Sequential Multi-location Auditing and the New York Food Stamps Program
24: Donald Rubin, Xiaoqin Wang, Li Yin & Elizabeth Zell: 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: A. Taylan Cemgil, Simon Godsill, Paul Peeling & Nick Whiteley: Bayesian Statistical Methods for Audio and Music Processing
26: Dave Higdon, Katrin Heitmann, Charles Nakhleh & Salman Habib: Combining Simulations and Physical Observations to Estimate Cosmological Parameters
27: Percy Liang, Michael Jordan & Dan Klein: Probabilistic Grammars and Hierarchical Dirichlet Processes
28: Herbert Lee, Matthew Taddy, Robert Gramacy & Genetha Gray: Designing and Analyzing a Circuit Device Experiment Using Treed Gaussian Processes
29: Raquel Prado: Multi-state Models for Mental Fatigue
Index
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.
