Bayesian Forecasting and Dynamic Models (Springer Series in Statistics)

West, Mike; Harrison, Jeff

ISBN 10: 0387970258 / ISBN 13: 9780387970257

Editore: Springer Verlag, 1989

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a very good hardcover; an ex-library copy with usual markings; internally pages bright and clean; ---------- SATISFACTION GUARANTEED --------- FAST, COURTEOUS SERVICE ------ ALL ORDERS SHIPPED WITH DELIVERY CONFIRMATION; Size: 6 x 9". Codice articolo K55736L12

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Titolo: Bayesian Forecasting and Dynamic Models (...

Casa editrice: Springer Verlag

Data di pubblicazione: 1989

Legatura: Hard Cover

Condizione: Very Good

Edizione: First Edition.

Tipologia articolo: Ex-Library

Descrizione articolo

Riassunto:

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.

Recensione:

"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)