Bayesian Forecasting and Dynamic Models:
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
New York, NY
Springer New York
1997
|
| Edition: | Second Edition |
| Series: | Springer Series in Statistics
|
| Subjects: | |
| Online Access: | Volltext |
| Item Description: | 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 |
| Physical Description: | 1 Online-Ressource (XIV, 682 p) |
| ISBN: | 9780387227771 9780387947259 |
| ISSN: | 0172-7397 |
| DOI: | 10.1007/b98971 |
Staff View
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| indexdate | 2024-07-10T01:21:04Z |
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| isbn | 9780387227771 9780387947259 |
| issn | 0172-7397 |
| language | English |
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| spelling | West, Mike Verfasser aut Bayesian Forecasting and Dynamic Models by Mike West, Jeff Harrison Second Edition New York, NY Springer New York 1997 1 Online-Ressource (XIV, 682 p) txt rdacontent c rdamedia cr rdacarrier Springer Series in Statistics 0172-7397 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 Statistics Distribution (Probability theory) Statistics, general Probability Theory and Stochastic Processes Statistik Prognose (DE-588)4047390-9 gnd rswk-swf Prognoseverfahren (DE-588)4358095-6 gnd rswk-swf Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd rswk-swf Dynamisches Modell (DE-588)4150932-8 gnd rswk-swf Entscheidungstheorie (DE-588)4138606-1 gnd rswk-swf Bayes-Verfahren (DE-588)4204326-8 gnd rswk-swf Prognose (DE-588)4047390-9 s Bayes-Verfahren (DE-588)4204326-8 s Dynamisches Modell (DE-588)4150932-8 s 1\p DE-604 Bayes-Entscheidungstheorie (DE-588)4144220-9 s 2\p DE-604 Entscheidungstheorie (DE-588)4138606-1 s 3\p DE-604 Prognoseverfahren (DE-588)4358095-6 s 4\p DE-604 Harrison, Jeff Sonstige oth https://doi.org/10.1007/b98971 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 3\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 4\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | West, Mike Bayesian Forecasting and Dynamic Models Statistics Distribution (Probability theory) Statistics, general Probability Theory and Stochastic Processes Statistik Prognose (DE-588)4047390-9 gnd Prognoseverfahren (DE-588)4358095-6 gnd Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd Dynamisches Modell (DE-588)4150932-8 gnd Entscheidungstheorie (DE-588)4138606-1 gnd Bayes-Verfahren (DE-588)4204326-8 gnd |
| subject_GND | (DE-588)4047390-9 (DE-588)4358095-6 (DE-588)4144220-9 (DE-588)4150932-8 (DE-588)4138606-1 (DE-588)4204326-8 |
| title | Bayesian Forecasting and Dynamic Models |
| title_auth | Bayesian Forecasting and Dynamic Models |
| title_exact_search | Bayesian Forecasting and Dynamic Models |
| title_full | Bayesian Forecasting and Dynamic Models by Mike West, Jeff Harrison |
| title_fullStr | Bayesian Forecasting and Dynamic Models by Mike West, Jeff Harrison |
| title_full_unstemmed | Bayesian Forecasting and Dynamic Models by Mike West, Jeff Harrison |
| title_short | Bayesian Forecasting and Dynamic Models |
| title_sort | bayesian forecasting and dynamic models |
| topic | Statistics Distribution (Probability theory) Statistics, general Probability Theory and Stochastic Processes Statistik Prognose (DE-588)4047390-9 gnd Prognoseverfahren (DE-588)4358095-6 gnd Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd Dynamisches Modell (DE-588)4150932-8 gnd Entscheidungstheorie (DE-588)4138606-1 gnd Bayes-Verfahren (DE-588)4204326-8 gnd |
| topic_facet | Statistics Distribution (Probability theory) Statistics, general Probability Theory and Stochastic Processes Statistik Prognose Prognoseverfahren Bayes-Entscheidungstheorie Dynamisches Modell Entscheidungstheorie Bayes-Verfahren |
| url | https://doi.org/10.1007/b98971 |
| work_keys_str_mv | AT westmike bayesianforecastinganddynamicmodels AT harrisonjeff bayesianforecastinganddynamicmodels |