Uncertain Judgements: Eliciting Experts' Probabilities

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Elicitation is the process of extracting expert knowledge about some unknown quantity or quantities, and formulating that information as a probability distribution. Elicitation is important in situations, such as modelling the safety of nuclear installations or assessing the risk of terrorist attacks, where expert knowledge is essentially the only source of good information. It also plays a major role in other contexts by augmenting scarce observational data, through the use of Bayesian statistical methods. However, elicitation is not a simple task, and practitioners need to be aware of a wide range of research findings in order to elicit expert judgements accurately and reliably. Uncertain Judgements introduces the area, before guiding the reader through the study of appropriate elicitation methods, illustrated by a variety of multi-disciplinary examples.

This is achieved by:

  • Presenting a methodological framework for the elicitation of expert knowledge incorporating findings from both statistical and psychological research.
  • Detailing techniques for the elicitation of a wide range of standard distributions, appropriate to the most common types of quantities.
  • Providing a comprehensive review of the available literature and pointing to the best practice methods and future research needs.
  • Using examples from many disciplines, including statistics, psychology, engineering and health sciences.
  • Including an extensive glossary of statistical and psychological terms.

An ideal source and guide for statisticians and psychologists with interests in expert judgement or practical applications of Bayesian analysis, Uncertain Judgements will also benefit decision-makers, risk analysts, engineers and researchers in the medical and social sciences.

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About the author

Professor Anthony O’Hagan is the Director of The Centre for Bayesian Statistics in Health Economics at the University of Sheffield. The Centre is a collaboration between the Department of Probability and Statistics and the School of Health and Related Research (ScHARR). The Department of Probability and Statistics is internationally respected for its research in Bayesian statistics, while ScHARR is one of the leading UK centres for economic evaluation.

Prof O’Hagan is an internationally leading expert in Bayesian Statistics.

Co-authors:

Professor Paul Gathwaite – Open University, Prof of Statistics, Maths and Computing

Dr Jeremy Oakley – Sheffield University

Professor John Brazier – Director of Health Economics Group, University of Sheffield

Dr Tim Rakow – University of Essex, Psychology Department

Dr Alireza Daneshkhah – University of Sheffield, Medical Statistics Department

Dr Jim Chilcott - School of Health Research, University of Sheffield, Department of OR

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Additional Information

Publisher
John Wiley & Sons
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Published on
Aug 30, 2006
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Pages
338
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ISBN
9780470033302
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Best For
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Language
English
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Genres
Mathematics / Probability & Statistics / General
Mathematics / Probability & Statistics / Stochastic Processes
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Content Protection
This content is DRM protected.
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Gareth James
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.

Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

Caitlin E. Buck
In summary, Bayesian methods are already seen by many as an essential tool to aid in formal chronology building in archaeology. At present, most researchers use packages like OxGal and BGal to make use of such tools and typically see them as radiocarbon calibration tools (indeed both are described as such on their own WWW welcome pages). On reflection, however, I think that it is clear that these packages offer more than just calibration, they are modest Bayesian chronological data interpretation environments. Given this observation, and the fact that the current tools are built on a sound foundation offlexible and scalable theory,I think that we are in a good position to move towards fully integrated tools for Bayesian chronology building. All of the current and planned research projects outlined above will contribute to the extension of the framework in one way or another. Since such work is motivated by a desire to provide practical solutions to real, current and pressing issues associated with chronology building, I feel sure that we can look forward to many more years of fast moving, productive and practical research in Bayesian chronology building. References Ammerman, A. J. and Cavalli-Sforza, 1. L. (1971). Measurement of the rate of spread of early farming in Europe. Man , 6, 674-688. Ammerman, A. J. and Cavalli-Sforza, 1. L. (1984). The Neolithic transition and the genetics of populations in Europe. Princeton University Press, Princeton. Anderson, A. , Allingham, B. and Smith, I. (1996a).
Caitlin E. Buck
In summary, Bayesian methods are already seen by many as an essential tool to aid in formal chronology building in archaeology. At present, most researchers use packages like OxGal and BGal to make use of such tools and typically see them as radiocarbon calibration tools (indeed both are described as such on their own WWW welcome pages). On reflection, however, I think that it is clear that these packages offer more than just calibration, they are modest Bayesian chronological data interpretation environments. Given this observation, and the fact that the current tools are built on a sound foundation offlexible and scalable theory,I think that we are in a good position to move towards fully integrated tools for Bayesian chronology building. All of the current and planned research projects outlined above will contribute to the extension of the framework in one way or another. Since such work is motivated by a desire to provide practical solutions to real, current and pressing issues associated with chronology building, I feel sure that we can look forward to many more years of fast moving, productive and practical research in Bayesian chronology building. References Ammerman, A. J. and Cavalli-Sforza, 1. L. (1971). Measurement of the rate of spread of early farming in Europe. Man , 6, 674-688. Ammerman, A. J. and Cavalli-Sforza, 1. L. (1984). The Neolithic transition and the genetics of populations in Europe. Princeton University Press, Princeton. Anderson, A. , Allingham, B. and Smith, I. (1996a).
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