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Time Series Analysis for the State-Space Model with R/Stan

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Time Series Analysis for the State-Space Model with R/Stan

Junichiro Hagiwara
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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.
Year:
2021
Edition:
1
Publisher:
Springer
Language:
english
Pages:
349
ISBN 10:
9811607117
ISBN 13:
9789811607110
File:
PDF, 11.11 MB
IPFS:
CID , CID Blake2b
english, 2021
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