Bayesian Forecasting and Dynamic Models / Edition 2

by Mike West, Jeff Harrison

Overview

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. This development has involved thorough investigation of mathematical and statistical aspects of forecasting models and related techniques. With this has come experience with applications in a variety of areas in commercial, industrial, scientific, and socio-economic fields. 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.

Product Details

ISBN-13:	9781475770988
Publisher:	Springer New York
Publication date:	03/08/2013
Series:	Springer Series in Statistics
Edition description:	2nd ed. 1997. Softcover reprint of the original 2nd ed. 1997
Pages:	682
Product dimensions:	6.10(w) x 9.25(h) x 0.06(d)

Table of Contents

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.

Editorial Reviews

Text provides graduate students of statistics (and professionals in the field) with a carefully detailed account of techniques (many of recent invention) which pertain to forecasting in time-dependent contexts. Sixteen chapters, many examples and exercises. Exceptionally lucid, if somewhat intimidating in its bulk. (NW) Annotation c. Book News, Inc., Portland, OR (booknews.com)

Booknews