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Sprache: Englisch
Verlag: Springer-Verlag New York Inc., New York, NY, 2013
ISBN 10: 1475770987 ISBN 13: 9781475770988
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Paperback. Zustand: new. Paperback. 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. This text is concerned with Bayesian learning, inference and forecasting in dynamic environments. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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hardcover. Zustand: New. In shrink wrap. Looks like an interesting title!
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Sprache: Englisch
Verlag: Springer-Verlag New York Inc., US, 2013
ISBN 10: 1475770987 ISBN 13: 9781475770988
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In den WarenkorbPaperback. Zustand: New. 2nd ed. 1997. Softcover reprint of the original 2nd ed. 1997.
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Sprache: Englisch
Verlag: Springer New York, Springer New York Mär 2013, 2013
ISBN 10: 1475770987 ISBN 13: 9781475770988
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Taschenbuch. Zustand: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 700 pp. Englisch.
Sprache: Englisch
Verlag: Springer New York, Springer New York, 2013
ISBN 10: 1475770987 ISBN 13: 9781475770988
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Thisdevelopmenthasinvolvedthoroughinvestigationofmathematical and 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.
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Zustand: New. pp. 700 2nd Edition.