Bayesian Forecasting and Dynamic Models (2nd ed.)

by Mike West, Jeff Harrison

Series: Springer Series in Statistics

Subject categories
ISBNs
  • 0387227776
  • 9780387947259
  • 9780387227771
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.

  • Springer New York; May 2006
  • ISBN: 9780387227771
  • Edition: 2
  • Read online, or download in DRM-free PDF (digitally watermarked) format
  • Title: Bayesian Forecasting and Dynamic Models
  • Series: Springer Series in Statistics
  • Author: Mike West; Jeff Harrison
  • Imprint: Springer
Subject categories
ISBNs
  • 0387227776
  • 9780387947259
  • 9780387227771
Subject categories
ISBNs
  • 0387227776
  • 9780387947259
  • 9780387227771