Bayesian Inference of State Space Models: Kalman Filtering and Beyond

Bayesian Inference of State Space Models: Kalman Filtering and Beyond provides a comprehensive introduction to Bayesian estimation and forecasting for state space models,with a focus on multivariate and non-linear models. It discusses various models,including linear Gaussian,non-linear and non-Gaussian models,with applications in signal processing,environmetrics,economics,and systems engineering. The book is accompanied by an R package,BTSA,and is suitable for upper undergraduate and graduate courses.

Format: Hardback
Length: 495 pages
Publication date: 13 November 2021
Publisher: Springer Nature Switzerland AG

Bayesian Inference of State Space Models: Kalman Filtering and Beyond is a comprehensive guide to Bayesian estimation and forecasting for state space models. The renowned Kalman filter, with its extensive extensions, takes center stage in this book. Univariate and multivariate models, encompassing linear Gaussian, non-linear, and non-Gaussian frameworks, are explored with applications in signal processing, environmetrics, economics, and systems engineering.

Over the past years, there has been a significant surge in the literature on Bayesian inference of state space models, particularly emphasizing multivariate models and non-linear and non-Gaussian models. The availability of vast time series data across various scientific and industrial fields, coupled with the advancements in low-cost computational capabilities, has paved the way for a multitude of statistical methods aimed at parameter estimation and forecasting. This book brings together a diverse range of these methods, offering an accessible and comprehensive introduction to state space models. Numerous real-life data sets from diverse disciplines are utilized to illustrate the methodologies and demonstrate their practical applications. The R package BTSA, developed specifically for this book, contains many of the algorithms and examples presented. The book is self-contained, with a chapter summarizing the prerequisites in undergraduate linear algebra, probability, and statistics.

An up-to-date and comprehensive exploration of state space methods, supported by real-life data sets and R code, this textbook caters to a wide audience, including students and scientists from statistics, systems engineering, signal processing, data science, finance, and econometrics. With numerous exercises in each chapter and convenient reminders of prerequisite knowledge, it serves as an invaluable resource for both novice and experienced practitioners in these fields.

Weight: 928g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030761233
Edition number: 1st ed. 2021