| Dewey Class |
519.5 |
| Title |
Bayesian Forecasting and Dynamic Models ([EBook] /) / by Mike West, Jeff Harrison. |
| Author |
West, Mike |
| Added Personal Name |
Harrison, Jeff author. |
| Other name(s) |
SpringerLink (Online service) |
| Publication |
New York, NY : : Springer New York : : Imprint: Springer, , 1989. |
| Physical Details |
XXI, 704 p. : online resource. |
| Series |
Springer Series in Statistics 0172-7397 |
| ISBN |
9781475793659 |
| Summary Note |
In this book we are concerned with Bayesian learning and forecast ing in dynamic environments. We describe the structure and theory of classes of dynamic models, and their uses in Bayesian forecasting. The principles, models and methods of Bayesian forecasting have been developed extensively during the last twenty years. This devel opment has involved thorough investigation of mathematical and sta tistical aspects of forecasting models and related techniques. With this has come experience with application in a variety of areas in commercial and industrial, scientific and socio-economic fields. In deed much of the technical development has been driven by the needs of forecasting practitioners. As a result, there now exists a relatively complete statistical and mathematical framework, although much of this is either not properly documented or not easily accessible. Our primary goals in writing this book have been to present our view of this approach to modelling and forecasting, and to provide a rea sonably complete text for advanced university students and research workers. The text is primarily intended for advanced undergraduate and postgraduate students in statistics and mathematics. In line with this objective we present thorough discussion of mathematical and statistical features of Bayesian analyses of dynamic models, with illustrations, examples and exercises in each Chapter.: |
| Contents note |
1 Introduction -- 2 Introduction to the DLM: The First-Order Polynomial Model -- 3 Introduction to the DLM: The Dynamic Regression Model -- 4 The Dynamic Linear Model -- 5 Univariate Time Series DLM Theory -- 6 Model Specification and Design -- 7 Polynomial Trend Models -- 8 Seasonal Models -- 9 Regression, Transfer Function and Noise Models -- 10 Illustrations and Extensions of Standard DLMS -- 11 Intervention and Monitoring -- 12 Multi-Process Models -- 13 Non-Linear Dynamic Models -- 14 Exponential Family Dynamic Models -- 15 Multivariate Modelling and Forecasting -- 16 Appendix: Distribution Theory and Linear Algebra -- Author Index. |
| System details note |
Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users) |
| Internet Site |
http://dx.doi.org/10.1007/978-1-4757-9365-9 |
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