Springer ebook

Bayesian Forecasting and Dynamic Models
Series: Springer Series in Statistics
West, Mike, Harrison, Jeff

2nd ed. 1997. Corr. 2nd printing, 1999, XIV, 680 p., 115 illus., Hardcover

ISBN: 978-0-387-94725-9

About this textbook
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. In addition to wide ranging updates to central material, the second edition includes many more exercises and covers new topics at the research and application frontiers of Bayesian forecastings.
Written for:
Researchers, graduate students

Table of contents
Introduction.- Introduction to the DLM: The first-order polynomial model.- Introduction to the DLM: The regression DLM.- 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. Bibliography.- Author Index.- Subject Index.