Bayesian forecasting and dynamic models / Mike West, Jeff Harrison
- 作者： West, Mike 1959
- 其他作者：
- 其他題名：
- Springer series in statistics
- 出版： New York : Springer c1997
- 叢書名： Springer series in statistics
- 主題： Bayesian statistical decision theory , Linear models (Statistics)
- 版本：2nd ed
- ISBN： 0387947256 (hbk.) :: NT1208
- 書目註：Includes bibliographical references (p. [652]-665) and index
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讀者標籤：
- 系統號： 005098176 | 機讀編目格式
館藏資訊
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.