Bayesian Forecasting and Dynamic Models (inbunden)
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Format
Inbunden (Hardback)
Språk
Engelska
Antal sidor
682
Utgivningsdatum
1997-01-01
Upplaga
2nd ed. 1997
Förlag
Springer-Verlag New York Inc.
Medarbetare
Bickel, P. (red.)
Illustratör/Fotograf
Tab 115 Abb Übers
Illustrationer
XIV, 682 p.
Dimensioner
242 x 163 x 45 mm
Vikt
1080 g
Antal komponenter
1
Komponenter
1 Hardback
ISBN
9780387947259

Bayesian Forecasting and Dynamic Models

av Mike West, Jeff Harrison
Inbunden,  Engelska, 1997-01-01
2002
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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.
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Mike West, Jeff Harrison

Innehållsförteckning

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.

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