Bayesian Forecasting and Dynamic Models Springer Series in Statistics

West, Mike and Jeff Harrison:

ISBN 10: 0387947256 / ISBN 13: 9780387947259

Published by Springer, 1997

About this Item

680 Seiten Der Erhaltungszustand des hier angebotenen Werks ist trotz seiner Bibliotheksnutzung sehr sauber und kann entsprechende Merkmale aufweisen (Rückenschild, Instituts-Stempel.). In ENGLISCHER Sprache. Sprache: Englisch Gewicht in Gramm: 1120.

Seller Inventory # 2157588

Bibliographic Details

• Title: Bayesian Forecasting and Dynamic Models Springer Series in Statistics
• Publisher: Springer
• Publication Date: 1997
• Language: English
• ISBN 10: 0387947256
• ISBN 13: 9780387947259
• Binding: gebundene Ausgabe
• Edition: 2. Auflage;.
• Condition: Gut
• Seller catalogs:
  • 90 Varia

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.