except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use of general descriptive names, trade names, trademarks, etc., in this publication, even if the former are not especially identified, is not to be taken as a sign that such names, as understood by the Trade Marks and Merchandise Marks Act, may accordingly be used freely by anyone.Camera-ready copy prepared by the authors using TpX. 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. The principles, models and methods of Bayesian forecasting have been developed extensively during the last twenty years. Much progress has been made with mathematical and statistical aspects of forecasting models and related techniques, and experience has been gained through application in a variety of areas in commercial and industrial, scientific and socio-economic fields. Indeed much of the technical development has been driven by the needs of forecasting practitioners. There now exists a relatively complete statistical and mathematical framework that is described and illustrated here for the first time in book form, presenting our view of this approach to modelling and forecasting. The book provides a self-contained text for advanced university students and research workers in business, economic and scientific disciplines, and forecasting practitioners. The material covers mathematical and statistical features of Bayesian analyses of dynamic models, with illustrations, examples and exercises in each chapter. In order that the ideas and techniques of Bayesian forecasting be accessible to students, research workers and practitioners alike, the book includes a number of examples and case studies involving real data, generously illustrated using computer generated graphs. These examples provide issues of modelling, data analysis and forecasting. Front Matter....Pages i-xxi Introduction....Pages 1-36 Introduction to the DLM: The First-Order Polynomial Model....Pages 37-74 Introduction to the DLM: The Dynamic Regression Model....Pages 75-104 The Dynamic Linear Model....Pages 105-141 Univariate Time Series DLM Theory....Pages 143-172 Model Specification and Design....Pages 173-200 Polynomial Trend Models....Pages 201-228 Seasonal Models....Pages 229-271 Regression, Transfer Function and Noise Models....Pages 273-318 Illustrations and Extensions of Standard DLMS....Pages 319-378 Intervention and Monitoring....Pages 379-436 Multi-Process Models....Pages 437-509 Non-Linear Dynamic Models....Pages 511-546 Exponential Family Dynamic Models....Pages 547-595 Multivariate Modelling and Forecasting....Pages 597-651 Appendix: Distribution Theory and Linear Algebra....Pages 653-676 Back Matter....Pages 677-704
