Bayesian Forecasting and Dynamic Models

West, Mike

ISBN 10: 0387947256 / ISBN 13: 9780387947259

Published by Springer, 1997

About this Item

This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In fair condition, suitable as a study copy. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,1200grams, ISBN:9780387947259.

Seller Inventory # 4138256

Bibliographic Details

• Title: Bayesian Forecasting and Dynamic Models
• Publisher: Springer
• Publication Date: 1997
• Language: English
• ISBN 10: 0387947256
• ISBN 13: 9780387947259
• Binding: Hardcover
• Edition: 2nd Edition
• Condition: Fair
• Seller catalogs:
  • Mathematics

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.