| 書目名稱 | Time Series Analysis for the State-Space Model with R/Stan |
| 編輯 | Junichiro Hagiwara |
| 視頻video | http://file.papertrans.cn/926/925440/925440.mp4 |
| 概述 | Provides a comprehensive and concrete illustration for the state-space model.Covers whole solutions through a consistent Bayesian approach: the batch method by MCMC using Stan and sequential ones by K |
| 圖書封面 |  |
| 描述 | This book provides a comprehensive and concrete illustration of time series analysis focusing on the state-space model, which has recently attracted increasing attention in a broad range of fields. The major feature of the book lies in its consistent Bayesian treatment regarding whole combinations of batch and sequential solutions for linear Gaussian and general state-space models: MCMC and Kalman/particle filter. The reader is given insight on flexible modeling in modern time series analysis. The main topics of the book deal with the state-space model, covering extensively, from introductory and exploratory methods to the latest advanced topics such as real-time structural change detection. Additionally, a practical exercise using R/Stan based on real data promotes understanding and enhances the reader’s analytical capability.??. |
| 出版日期 | Book 2021 |
| 關(guān)鍵詞 | Time Series Analysis; State-Space Model; Kalman Filter; MCMC; Particle Filter; Baysian Inference |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-981-16-0711-0 |
| isbn_softcover | 978-981-16-0713-4 |
| isbn_ebook | 978-981-16-0711-0 |
| copyright | Springer Nature Singapore Pte Ltd. 2021 |
|Archiver|手機版|小黑屋|
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