| 書(shū)目名稱 | Dynamic Linear Models with R |
| 編輯 | Patrizia Campagnoli,Sonia Petrone,Giovanni Petris |
| 視頻video | http://file.papertrans.cn/284/283625/283625.mp4 |
| 概述 | Fully worked-out examples in the freely available statistical software R.Guides the reader in a friendly way from the basics of the Bayesian approach to its practical application to time series analys |
| 叢書(shū)名稱 | Use R! |
| 圖書(shū)封面 |  |
| 描述 | .State space models have gained tremendous popularity in recent years in as disparate fields as engineering, economics, genetics and ecology. After a detailed introduction to general state space models, this book focuses on dynamic linear models, emphasizing their Bayesian analysis. Whenever possible it is shown how to compute estimates and forecasts in closed form; for more complex models, simulation techniques are used. A final chapter covers modern sequential Monte Carlo algorithms.. .The book illustrates all the fundamental steps needed to use dynamic linear models in practice, using R. Many detailed examples based on real data sets are provided to show how to set up a specific model, estimate its parameters, and use it for forecasting. All the code used in the book is available online.. .No prior knowledge of Bayesian statistics or time series analysis is required, although familiarity with basic statistics and R is assumed.. |
| 出版日期 | Book 2009 |
| 關(guān)鍵詞 | Bayesian inference; Time series; bayesian statistics; dynamic models; state space models; time series ana |
| 版次 | 1 |
| doi | https://doi.org/10.1007/b135794 |
| isbn_softcover | 978-0-387-77237-0 |
| isbn_ebook | 978-0-387-77238-7Series ISSN 2197-5736 Series E-ISSN 2197-5744 |
| issn_series | 2197-5736 |
| copyright | Springer-Verlag New York 2009 |
|Archiver|手機(jī)版|小黑屋|
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