Overview
- Authors:
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Mike West
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Institute of Statistics and Decision Sciences, Duke University, Durham, USA
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Jeff Harrison
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Department of Statistics, University of Warwick, Coventry, UK
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Table of contents (16 chapters)
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Back Matter
Pages 677-704
About this book
In this book we are concerned with Bayesian learning and forecast ing in dynamic environments. We describe the structure and theory of classes of dynamic models, and their uses in Bayesian forecasting. The principles, models and methods of Bayesian forecasting have been developed extensively during the last twenty years. This devel opment has involved thorough investigation of mathematical and sta tistical aspects of forecasting models and related techniques. With this has come experience with application in a variety of areas in commercial and industrial, scientific and socio-economic fields. In deed much of the technical development has been driven by the needs of forecasting practitioners. As a result, there now exists a relatively complete statistical and mathematical framework, although much of this is either not properly documented or not easily accessible. Our primary goals in writing this book have been to present our view of this approach to modelling and forecasting, and to provide a rea sonably complete text for advanced university students and research workers. The text is primarily intended for advanced undergraduate and postgraduate students in statistics and mathematics. In line with this objective we present thorough discussion of mathematical and statistical features of Bayesian analyses of dynamic models, with illustrations, examples and exercises in each Chapter.
Reviews
"The authors are well known to anyone with an interest in forecasting and time series modelling.... Anyone familiar with the authors' work will wish to read this book."
(Mathematical Reviews)
Authors and Affiliations
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Institute of Statistics and Decision Sciences, Duke University, Durham, USA
Mike West
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Department of Statistics, University of Warwick, Coventry, UK
Jeff Harrison
