The Oxford Handbook of Applied Bayesian Analysis by Anthony O' HaganThe Oxford Handbook of Applied Bayesian Analysis by Anthony O' Hagan

The Oxford Handbook of Applied Bayesian Analysis

EditorAnthony O' Hagan, Mike West

Paperback | December 14, 2013

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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 increasinglyrealistic 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 importantand demanding problems. The chapters are grouped into five general areas: Biomedical and Health Sciences; Industry, Economics and Finance; Environment and Ecology; Policy, Political and Social Sciences; and Natural and 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.
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 Sheffiel...
Title:The Oxford Handbook of Applied Bayesian AnalysisFormat:PaperbackDimensions:928 pages, 9.69 × 6.73 × 0 inPublished:December 14, 2013Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0198703171

ISBN - 13:9780198703174


Table of Contents

PrefacePart I - Biomedical and Health Sciences1. David Dunson: Flexible Bayes Regression of Epidemiologic Data2. Peter Green, Kanti Mardia, Vysaul Nyirongo and Yann Ruffieux: Bayesian Modelling for Matching and Alignment of Biomolecules3. Jerry Cheng and David Madigan: Bayesian Approaches to Aspects of the Vioxx Trials: Non-ignorable Dropout and Sequential Meta-Analysis4. Jeremy Oakley and Helen Clough: Sensitivity Analysis in Microbial Risk Assessment: Vero-cytotoxigenic iE.coli/i O157 in Farm-Pasteurised Milk5. Alexandra Schmidt, Jennifer Hoeting, Joao Batista Pereira and Pedro Paulo Vieira: Mapping Malaria in the Amazon Rain Forest: a Spatio-Temporal Mixture Model6. Dan Merl, Joseph Lucas, Joseph Nevins, Haige Shenz and Mike West: Trans-Study Projection of Genomic Biomarkers in Analysis of Oncogene Deregulation and Breast Cancer7. D. A. Henderson, R.J. Boys, C.J. Proctor and D.J. Wilkinson: Linking Systems Biology Models to Data: a Stochastic Kinetic Model of p53 OscillationsPart II - Industry, Economics and Finance8. Elmira Popova, David Morton, Paul Damien and Tim Hanson: Bayesian Analysis and Decisions in Nuclear Power Plant Maintenance9. Jonathan Cumming and Michael Goldstein: Bayes Linear Uncertainty Analysis for Oil Reservoirs Based on Multiscale Computer Experiments10. Antonio Pievatolo and Fabrizio Ruggeri: Bayesian Modelling of Train Doors Reliability11. Marco Ferreira, Adelmo Bertoldey and Scott Holan: Analysis of Economic Data With Multiscale Spatio-temporal Models12. Hedibert Lopes and Nicholas Polson: Extracting SandP500 and NASDAQ Volatility: The Credit Crisis of 2007-200813. Jose Mario Quintana, Carlos Carvalho, James Scott and Thomas Costigliola: Futures Markets, Bayesian Forecasting, and Risk Modeling14. Jesus Fernandez-Villaverde, Pablo Guerron-Quintana and Juan Rubio-Ramirez: The New Macroeconometrics: A Bayesian ApproachPart III - Environment and Ecology15. Peter Challenor, Doug McNeall and James Gattiker: Assessing The Probability of Rare Climate Events16. James Clark, Dave Bell, Michael Dietze, Michelle Hersh, Ines Ibanez, Shannon LaDeau, Sean McMahon, Jessica Metcalf, Emily Moran, Luke Pangle and Mike Wolosin: Models for Demography of Plant Populations17. Alan Gelfand and Sujit K. Sahu: Combining Monitoring Data and Computer Model Output in Assessing Environmental Exposure18. Samantha Low Choy, Justine Murray, Allan James and Kerrie Mengersen: Indirect Elicitation From Ecological Experts: From Methods and Software to Habitat Modelling and Rock-Wallabies19. Claudia Tebaldi and Richard Smith: Characterizing the Uncertainty of Climate Change Projections Using Hierarchical ModelsPart IV - Policy, Political and Social Sciences20. Carlos Carvalho and Jill Rickershauser: Volatility in Prediction Markets: A Measure of Information Flow in Political Campaigns21. Philip Dawid, Julia Mortera and Paola Vicard: Paternity Testing Allowing for Uncertain Mutation Rates22. Dani Gamerman, Tufi Soares and Flavio Goncalves: Bayesian Analysis in Item Response Theory Applied to a Large-scale Educational Assessment23. Karl Heiner, Marc Kennedy and Anthony O'Hagan: Sequential Multi-location Auditing and the New York Food Stamps Program24. Donald Rubin, Xiaoqin Wang, Li Yin and Elizabeth Zell: Bayesian Causal Inference: Approaches to Estimating the Effect of Treating Hospital Type on Cancer Survival in Sweden Using Principal StratificationPart V - Natural and Engineering Sciences25. A. Taylan Cemgil, Simon Godsill, Paul Peeling and Nick Whiteley: Bayesian Statistical Methods for Audio and Music Processing26. Dave Higdon, Katrin Heitmann, Charles Nakhleh and Salman Habib: Combining Simulations and Physical Observations to Estimate Cosmological Parameters27. Percy Liang, Michael Jordan and Dan Klein: Probabilistic Grammars and Hierarchical Dirichlet Processes28. Herbert Lee, Matthew Taddy, Robert Gramacy and Genetha Gray: Designing and Analyzing a Circuit Device Experiment Using Treed Gaussian Processes29. Raquel Prado: Multi-state Models for Mental FatigueIndex