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＜電子ブック＞
Bayesian Forecasting and Dynamic Models / by Mike West, Jeff Harrison
(Springer Series in Statistics. ISSN:2197568X)

版	2nd ed. 1997.
出版者	(New York, NY : Springer New York : Imprint: Springer)
出版年	1997
本文言語	英語
大きさ	XIV, 682 p : online resource
著者標目	*West, Mike author Harrison, Jeff author SpringerLink (Online service)
件　名	LCSH:Probabilities LCSH:Econometrics FREE:Probability Theory FREE:Quantitative Economics
一般注記	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 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 HTTP:URL=https://doi.org/10.1007/b98971

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