Overview
- 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
Part of the book series: Springer Texts in Statistics (STS)
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Table of contents (8 chapters)
Keywords
About this book
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.
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Bibliographic Information
Book Title: Bayesian Inference of State Space Models
Book Subtitle: Kalman Filtering and Beyond
Authors: Kostas Triantafyllopoulos
Series Title: Springer Texts in Statistics
DOI: https://doi.org/10.1007/978-3-030-76124-0
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-76123-3Published: 13 November 2021
Softcover ISBN: 978-3-030-76126-4Published: 13 November 2022
eBook ISBN: 978-3-030-76124-0Published: 12 November 2021
Series ISSN: 1431-875X
Series E-ISSN: 2197-4136
Edition Number: 1
Number of Pages: XV, 495
Number of Illustrations: 54 b/w illustrations, 33 illustrations in colour