| 期刊全稱 | Bayesian Forecasting and Dynamic Models | | 影響因子2023 | Mike West,Jeff Harrison | | 視頻video | http://file.papertrans.cn/182/181842/181842.mp4 | | 學(xué)科分類 | Springer Series in Statistics | | 圖書封面 |  | | 影響因子 | 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. | | Pindex | Textbook 1997Latest edition |
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