Springer Texts in Statistics
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Bayesian Inference of State Space Models

Kalman Filtering and Beyond

Autoren: Triantafyllopoulos, Kostas

  • Provides a comprehensive account of linear and non-linear state space modelling, including R
  • Discusses in detail the applications to financial time series, dynamic systems, and control
  • Reviews simulation-based Bayesian inference, such as Markov chain Monte Carlo and sequential Monte Carlo methods
  • Demonstrates how state space modelling can be applied using R
Weitere Vorteile

Dieses Buch kaufen

eBook 93,08 €
Preis für Deutschland (Brutto)
  • Die eBook-Version des Titels ist in Kürze verfügbar
  • Erscheinungstermin: 10. November 2021
  • ISBN 978-3-030-76124-0
  • Versehen mit digitalem Wasserzeichen, DRM-frei
  • Erhältliche Formate:
  • eBooks sind auf allen Endgeräten nutzbar
Hardcover 117,69 €
Preis für Deutschland (Brutto)
Über dieses Lehrbuch

Bayesian Inference of State Space Models: Kalman Filtering and Beyond offers a comprehensive introduction to Bayesian estimation and forecasting for state space models. The celebrated Kalman filter, with its numerous extensions, takes centre stage in the book. Univariate and multivariate models, linear Gaussian, non-linear and non-Gaussian models are discussed with applications to signal processing, environmetrics, economics and systems engineering.

Over the past years there has been a growing literature on Bayesian inference of state space models, focusing on multivariate models as well as on non-linear and non-Gaussian models. The availability of time series data in many fields of science and industry on the one hand, and the development of low-cost computational capabilities on the other, have resulted in a wealth of statistical methods aimed at parameter estimation and forecasting. This book brings together many of these methods, presenting an accessible and comprehensive introduction to state space models. A number of data sets from different disciplines are used to illustrate the methods and show how they are applied in practice. The R package BTSA, created for the book, includes many of the algorithms and examples presented. The book is essentially self-contained and includes a chapter summarising the prerequisites in undergraduate linear algebra, probability and statistics.

An up-to-date and complete account of state space methods, illustrated by real-life data sets and R code, this textbook will appeal to a wide range of students and scientists, notably in the disciplines of statistics, systems engineering, signal processing, data science, finance and econometrics. With numerous exercises in each chapter, and prerequisite knowledge conveniently recalled, it is suitable for upper undergraduate and graduate courses.

Über die Autor*innen

Kostas Triantafyllopoulos is a Senior Lecturer at the School of Mathematics and Statistics of the University of Sheffield. He holds a PhD in Statistics from the University of Warwick and prior to Sheffield worked as a Research Associate at the University of Bristol and as a Lecturer at the University of Newcastle upon Tyne. His research interests include Bayesian inference of time series models and statistical process control. He has published widely and is involved in research grants including the Nuffield Foundation, the NHS and the Engineering and Physical Sciences Research Council (UK). He has wide teaching experience in statistics and has supervised a number of doctoral students and postdoctoral fellows.

Dieses Buch kaufen

eBook 93,08 €
Preis für Deutschland (Brutto)
  • Die eBook-Version des Titels ist in Kürze verfügbar
  • Erscheinungstermin: 10. November 2021
  • ISBN 978-3-030-76124-0
  • Versehen mit digitalem Wasserzeichen, DRM-frei
  • Erhältliche Formate:
  • eBooks sind auf allen Endgeräten nutzbar
Hardcover 117,69 €
Preis für Deutschland (Brutto)
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Bibliografische Information

Bibliographic Information
Buchtitel
Bayesian Inference of State Space Models
Buchuntertitel
Kalman Filtering and Beyond
Autoren
Titel der Buchreihe
Springer Texts in Statistics
Copyright
2021
Verlag
Springer International Publishing
Copyright Inhaber
The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
eBook ISBN
978-3-030-76124-0
DOI
10.1007/978-3-030-76124-0
Hardcover ISBN
978-3-030-76123-3
Buchreihen ISSN
1431-875X
Auflage
1
Seitenzahl
XVI, 498
Anzahl der Bilder
54 schwarz-weiß Abbildungen, 33 Abbildungen in Farbe
Themen