Bayesian Forecasting and Dynamic Models

  • Mike West
  • Jeff Harrison

Part of the Springer Series in Statistics book series (SSS)

Table of contents

  1. Front Matter
    Pages i-xxi
  2. Mike West, Jeff Harrison
    Pages 1-36
  3. Mike West, Jeff Harrison
    Pages 37-74
  4. Mike West, Jeff Harrison
    Pages 75-104
  5. Mike West, Jeff Harrison
    Pages 105-141
  6. Mike West, Jeff Harrison
    Pages 143-172
  7. Mike West, Jeff Harrison
    Pages 173-200
  8. Mike West, Jeff Harrison
    Pages 201-228
  9. Mike West, Jeff Harrison
    Pages 229-271
  10. Mike West, Jeff Harrison
    Pages 273-318
  11. Mike West, Jeff Harrison
    Pages 319-378
  12. Mike West, Jeff Harrison
    Pages 379-436
  13. Mike West, Jeff Harrison
    Pages 437-509
  14. Mike West, Jeff Harrison
    Pages 511-546
  15. Mike West, Jeff Harrison
    Pages 547-595
  16. Mike West, Jeff Harrison
    Pages 597-651
  17. Mike West, Jeff Harrison
    Pages 653-676
  18. Back Matter
    Pages 677-704

About this book

Introduction

In this book we are concerned with Bayesian learning and forecast­ ing in dynamic environments. We describe the structure and theory of classes of dynamic models, and their uses in Bayesian forecasting. The principles, models and methods of Bayesian forecasting have been developed extensively during the last twenty years. This devel­ opment has involved thorough investigation of mathematical and sta­ tistical aspects of forecasting models and related techniques. With this has come experience with application in a variety of areas in commercial and industrial, scientific and socio-economic fields. In­ deed much of the technical development has been driven by the needs of forecasting practitioners. As a result, there now exists a relatively complete statistical and mathematical framework, although much of this is either not properly documented or not easily accessible. Our primary goals in writing this book have been to present our view of this approach to modelling and forecasting, and to provide a rea­ sonably complete text for advanced university students and research workers. The text is primarily intended for advanced undergraduate and postgraduate students in statistics and mathematics. In line with this objective we present thorough discussion of mathematical and statistical features of Bayesian analyses of dynamic models, with illustrations, examples and exercises in each Chapter.

Keywords

data analysis forecasting modeling regression time series

Authors and affiliations

  • Mike West
    • 1
  • Jeff Harrison
    • 2
  1. 1.Institute of Statistics and Decision SciencesDuke UniversityDurhamUSA
  2. 2.Department of StatisticsUniversity of WarwickCoventryUK

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4757-9365-9
  • Copyright Information Springer-Verlag New York 1989
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4757-9367-3
  • Online ISBN 978-1-4757-9365-9
  • Series Print ISSN 0172-7397
  • About this book