The Oxford Handbook of Applied Bayesian Analysis

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Overview


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.

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Editorial Reviews

From the Publisher

"The breadth of topics covered in this collection is impressive, from plant demography to nuclear power plant maintenance to the reliability of train doors. Some fascinating application areas were ones I had never encountered previously, such as music-to-score transcription: creating a written musical score from raw audio signals, as discussed be Cemgil et al. The inclusion of an expert elicitation example by Choy et al. was also very welcome." -- JASA

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Product Details

  • ISBN-13: 9780199548903
  • Publisher: Oxford University Press, USA
  • Publication date: 5/13/2010
  • Series: Oxford Handbooks Series
  • Pages: 896
  • Product dimensions: 7.10 (w) x 9.80 (h) x 2.00 (d)

Meet the Author

Tony 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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Table of Contents

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

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