Bayesian Forecasting and Dynamic Models (Springer Series in Statistics)

West, Mike; Harrison, Jeff

ISBN 10: 0387947256 / ISBN 13: 9780387947259

Published by Springer (edition 2nd), 1997

About this Item

It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Seller Inventory # 0387947256-11-1

Bibliographic Details

• Title: Bayesian Forecasting and Dynamic Models (Springer Series in Statistics)
• Publisher: Springer (edition 2nd)
• Publication Date: 1997
• Language: English
• ISBN 10: 0387947256
• ISBN 13: 9780387947259
• Binding: Hardcover
• Edition: 2nd.
• Condition: Very Good

About this title

Synopsis

This text is concerned with Bayesian learning, inference and forecasting in dynamic environments. We describe the structure and theory of classes of dynamic models and their uses in forecasting and time series analysis. The principles, models and methods of Bayesian forecasting and time - ries analysis have been developed extensively during the last thirty years. Thisdevelopmenthasinvolvedthoroughinvestigationofmathematicaland statistical aspects of forecasting models and related techniques. With this has come experience with applications in a variety of areas in commercial, industrial, scienti?c, and socio-economic ?elds. Much of the technical - velopment has been driven by the needs of forecasting practitioners and applied researchers. As a result, there now exists a relatively complete statistical and mathematical framework, presented and illustrated here. In writing and revising this book, our primary goals have been to present a reasonably comprehensive view of Bayesian ideas and methods in m- elling and forecasting, particularly to provide a solid reference source for advanced university students and research workers.

From the Back Cover

The second edition of this book includes revised, updated, and additional material on the structure, theory, and application of classes of dynamic models in Bayesian time series analysis and forecasting.