Bayesian Forecasting and Dynamic Models (Springer Series in Statistics) - Hardcover

West, Mike; Harrison, Jeff

Hardcover

ISBN 10: 0387947256 ISBN 13: 9780387947259

Publisher: Springer, 1997

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.

About this title

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.

Edition details

• Publisher: Springer
• Publication date: 1997
• Language: English
• ISBN 10: 0387947256
• ISBN 13: 9780387947259
• Binding: Hardcover
• Edition number: 2
• Number of pages: 696