The Bayesian approach to statistics and decision analysis has now established itself as a powerful and very practical tool to support the non-statistical, but otherwise technical modeller. Recent advances in computational techniques have released its potential to an extent where even very complicated scenarios are amenable to Bayesian analysis whether conducted by the statistical community or others. The purpose of this book is twofold. Firstly it has been written to advertise the advantages a Bayesian analysis can bring. New statistical and decision models can be tailored to the unique beliefs, values and needs of the user and the implications of the data she collects can be analyzed with reference to this underlying structure. The second purpose is to provide an analyst, wanting to apply the Bayesian methodology, with a resource of examples and techniques so she may be better informed about the scope of opportunities and the pitfalls inherent in such an analysis.
Rezensionen / Stimmen
The first introductory chapter...is one of the best short expositions of the Bayesian analysis which can be found in literature. An important book, each example of the Bayesian approach contained is a very interesting one. -- Statistics in Transition ...an essential addition to any departmental library of mathematics, econometry, control, management or informatics. -- The Statistician, No. 47, 1998 ... a useful overview of Bayesian methods ... interesting and broad in scope ... I can enthusiastically recommend The Practice of Bayesian Analysis as stimulating reading... -- Technometrics, Feb 1999, Vol 41, No 1
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
Illustrationen
Maße
Höhe: 241 mm
Breite: 162 mm
Dicke: 18 mm
Gewicht
ISBN-13
978-0-340-66240-3 (9780340662403)
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Schweitzer Klassifikation
Bayesian analysis; mathematical review of Swedish Bayesian methodology for nuclear plant reliability data bases; Baye's theorem and decision support in clinical medicine; developing a bayesian linear support system for a brewery; event conditional attribute modelling in decision making when there is a threat of a nuclear accident; the rise and fall of a risk-based priority system - lessons from DOE's environmental restoration priority system; optimal decisions that reduce flood damage along the meuse - an uncertainty analysis; the ABLE story - bayesian asset management in the water industry; model elicitation in competitive markets; building a bayesian model in a scientific environment - managing uncertainty after an accident; project evaluation in drug development - a case study in collaboration; bayesian methods in reservoir operation - the Zambezi river case.
