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.

The Oxford Handbook of Applied Bayesian Analysis (Oxford Handbooks)

Science & Math

West, Mike

O'Hagan, Anthony

Mathematics

Oxford University Press

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 &amp; Health Sciences; Industry, Economics &amp; Finance; Environment &amp; Ecology; Policy, Political &amp; Social Sciences; and Natural &amp; 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.

The Bayesian approach to statistics is now widely accepted as theoretically sound and practically viable. Enormous advances in Bayesian methodology in recent years have resulted in a great expansion of applications of Bayesian statistics in a wide variety of fields. This important addition to the series provides a comprehensive and readable treatment of modern Bayesian theory.

The Advanced Theory of Statistics, Vol. 2B: Bayesian Inference

Business & Money

Education & Reference

Hodder Education Publishers

This set offers the complete, classic Kendall's Advanced Theory of Statistics in a single value-for-money pack. The latest set includes the brand new second edition of the popular "Volume 2B: Bayesian Inference," along with the sixth editions of "Volume I: Distribution Theory," and "Volume 2A: Classical Inference and the Linear Model."

Kendall's Advanced Theory of Statistics

Ord, Keith

Arnold, Steven

Stuart, the late Alan

Forster, Jon

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

Uncertain Judgements: Eliciting Experts' Probabilities

Rakow, Tim

Eiser, J. Richard

Garthwaite, Paul H.

Buck, Caitlin E.

Daneshkhah, Alireza

Jenkinson, David J.

Oakley, Jeremy E.

wiley

In recent years enormous advances in Bayesian methodology have resulted in a great expansion regarding the applications of Bayesian statistics in a wide range of fields. This up-to-date volume is designed to meet the growing need for a comprehensive treatment of modern Bayesian theory in readable form. Includes chapters on robustness and computation.

Kendall's Advanced Theory of Statistics: The Advanced Theory of Statistics, Vol. 2B: Bayesian Inference

Kendall's Advanced Theory of Statistics and Kendall's Library of Statistics The development of modern statistical theory in the past fifty years is reflected in the history of the late Sir Maurice Kenfall's volumes The Advanced Theory of Statistics. The Advanced Theory began life as a two-volume work, and since its first appearance in 1943, has been an indispensable source for the core theory of classical statistics. With Bayesian Inference, the same high standard has been applied to this important and exciting new body of theory.

Kendall's Advanced Theory of Statistic 2B

This book is an elementary and practical introduction to probability theory. It differs from other introductory texts in two important respects. First, the per sonal (or subjective) view of probability is adopted throughout. Second, emphasis is placed on how values are assigned to probabilities in practice, i.e. the measurement of probabilities. The personal approach to probability is in many ways more natural than other current formulations, and can also provide a broader view of the subject. It thus has a unifying effect. It has also assumed great importance recently because of the growth of Bayesian Statistics. Personal probability is essential for modern Bayesian methods, and it can be difficult for students who have learnt a different view of probability to adapt to Bayesian thinking. This book has been produced in response to that difficulty, to present a thorough introduction to probability from scratch, and entirely in the personal framework.

Probability: Methods and measurement

Reference

Springer

See the best price to sell, buy, or rent books by O'Hagan, Anthony. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by O'Hagan, Anthony

See the best price to sell, buy, or rent books by O'Hagan, Anthony. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

The Oxford Handbook of Applied Bayesian Analysis (Oxford Handbooks)

The Oxford Handbook of Applied Bayesian Analysis (Oxford Handbooks)

The Advanced Theory of Statistics, Vol. 2B: Bayesian Inference

Kendall's Advanced Theory of Statistics

Uncertain Judgements: Eliciting Experts' Probabilities

Kendall's Advanced Theory of Statistics: The Advanced Theory of Statistics, Vol. 2B: Bayesian Inference

Kendall's Advanced Theory of Statistic 2B

Probability: Methods and measurement