Bayesian Forecasting and Dynamic Models

WEST, Mike; HARRISON, Jeff

ISBN 10: 0387947256 / ISBN 13: 9780387947259

Edité par Springer, New York, 1997

A propos de cet article

Springer Series in Statistics. xiv, 680, [1] p. 24 cm. 115 figures. Yellow hardcover. Topics: the dynamic linear model; DLM theory; polynomial trend, seasonal, regression, autoregression, and related models; multi-process models; non-linear dynamic models; exponential family dynamic models; multivariate modelling and forecasting; distribution theory and linear algebra; etc. Bibliography and index. N° de réf. du vendeur 116416

Détails bibliographiques

Titre : Bayesian Forecasting and Dynamic Models

Éditeur : Springer, New York

Date d'édition : 1997

Reliure : Hardcover

Etat : As new

Edition : Second Edition.

A propos de ce titre

Synopsis :

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, scienti?c, 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.