You might also like

More about Bayesian Forecasting and Dynamic Models

• Book details
• Synopsis
• Trending among others

You get 138 loyalty points

Book synopsis

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.

Book details

• Full title: Bayesian Forecasting and Dynamic Models
• Author: Mike West, Jeff Harrison
• Language: English
• Binding: Paperback
• Number of pages: 700
• EAN: 9781475770971
• ISBN: 1475770979
• ID: 50419832
• Publisher: Springer Nature B.V.
• Weight: 966 g
• Dimensions: 234 × 156 × 36 mm
• Date of publishing: 17. March 2013

Trending among others

---

---

---