Bayesian Forecasting and Dynamic Models (Springer Series in Statistics) ハードカバー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. 値下げ歓迎中です!
Bayesian Forecasting and Dynamic Models | SpringerLink
Bayesian Forecasting and Dynamic Models | SpringerLink
Bayesian Forecasting and Dynamic Models (Springer Series in Statistics): Amazon.co.uk: West, Mike, Harrison, Jeff: 9780387947259: Books
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Bayesian Forecasting and Dynamic Models | SpringerLink
Bayesian Forecasting and Dynamic Models | SpringerLink
Bayesian Forecasting and Dynamic Models (Springer Series in Statistics): Amazon.co.uk: West, Mike, Harrison, Jeff: 9780387947259: Books
Bayesian dynamic forecasting | New in Stata 17
Forecasting with Bayesian Dynamic Generalized Linear Models in Python | by Ryan Clukey | Towards Data Science
Forecasting with Dynamic Linear Model (DLM) — Pyro Tutorials 1.9.1 documentation
Application of Bayesian Statistics to Dynamic Forecasting - Forecast360
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BIBLIO | Bayesian Forecasting and Dynamic Models by WEST, Mike; HARRISON, Jeff | Hardcover | 1997 | Springer | 9780387947259