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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.
Produkteigenschaften
- Artikelnummer: 9789811607134
- Medium: Buch
- ISBN: 978-981-16-0713-4
- Verlag: Springer Nature Singapore
- Erscheinungstermin: 01.09.2022
- Sprache(n): Englisch
- Auflage: 1. Auflage 2021
- Produktform: Kartoniert
- Gewicht: 552 g
- Seiten: 347
- Format (B x H x T): 155 x 235 x 20 mm
- Ausgabetyp: Kein, Unbekannt