Bayesian Forecasting and Dynamic Models 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 scientic 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. to the DLM: The First Order Polynomial Model to the DLM: The Dynamic Regression Model The Dynamic Linear Model Univariate Time Series DLM Theory Model Specification and Design Polynomial Trend Models Seasonal Models Regression Autoregression and Related Models Illustrations and Extensions of Standard DLMs Intervention and Monitoring Multi Process Models Non Linear Dynamic Models: Analytic and Numerical Approximations Exponential Family Dynamic Models Simulation Based Methods in Dynamic Models Multivariate Modelling and Forecasting Distribution Theory and Linear Algebra.