Springer Texts in Statistics
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Bayesian Inference of State Space Models

Kalman Filtering and Beyond

Authors: 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
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eBook $109.00
price for USA in USD
  • Due: December 28, 2021
  • ISBN 978-3-030-76124-0
  • Digitally watermarked, DRM-free
  • Included format:
  • ebooks can be used on all reading devices
Hardcover $139.99
price for USA in USD
  • Customers within the U.S. and Canada please contact Customer Service at +1-800-777-4643, Latin America please contact us at +1-212-460-1500 (24 hours a day, 7 days a week). Pre-ordered printed titles are excluded from promotions.
  • Due: November 30, 2021
  • ISBN 978-3-030-76123-3
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Shipping restrictions
About this Textbook

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.

About the authors

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.

Buy this book

eBook $109.00
price for USA in USD
  • Due: December 28, 2021
  • ISBN 978-3-030-76124-0
  • Digitally watermarked, DRM-free
  • Included format:
  • ebooks can be used on all reading devices
Hardcover $139.99
price for USA in USD
  • Customers within the U.S. and Canada please contact Customer Service at +1-800-777-4643, Latin America please contact us at +1-212-460-1500 (24 hours a day, 7 days a week). Pre-ordered printed titles are excluded from promotions.
  • Due: November 30, 2021
  • ISBN 978-3-030-76123-3
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Shipping restrictions
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Bibliographic Information

Bibliographic Information
Book Title
Bayesian Inference of State Space Models
Book Subtitle
Kalman Filtering and Beyond
Authors
Series Title
Springer Texts in Statistics
Copyright
2021
Publisher
Springer International Publishing
Copyright Holder
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
Series ISSN
1431-875X
Edition Number
1
Number of Pages
XVI, 498
Number of Illustrations
54 b/w illustrations, 33 illustrations in colour
Topics