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Bayesian inference of state space mo...

Triantafyllopoulos, Kostas.

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Bayesian inference of state space models = Kalman filtering and beyond /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Bayesian inference of state space models/ by Kostas Triantafyllopoulos.
其他題名:	Kalman filtering and beyond /
作者:	Triantafyllopoulos, Kostas.
出版者:	Cham :Springer International Publishing : : 2021.,
面頁冊數:	xv, 495 p. :ill. (some col.), digital ;24 cm.
內容註:	1 State Space Models -- 2 Matrix Algebra, Probability and Statistics -- 3 The Kalman Filter -- 4 Model Specification and Model Performance -- 5 Multivariate State Space Models -- 6 Non-linear and non-Gaussian State Space Models -- 7 The State Space Model in Finance -- 8 Dynamic Systems and Control -- References -- Index.
Contained By:	Springer Nature eBook
標題:	Kalman filtering. -
電子資源:	https://doi.org/10.1007/978-3-030-76124-0
ISBN:	9783030761240

Bayesian inference of state space models = Kalman filtering and beyond /
Triantafyllopoulos, Kostas.

Bayesian inference of state space modelsKalman filtering and beyond /[electronic resource] :by Kostas Triantafyllopoulos. - Cham :Springer International Publishing :2021. - xv, 495 p. :ill. (some col.), digital ;24 cm. - Springer texts in statistics,2197-4136. - Springer texts in statistics..

1 State Space Models -- 2 Matrix Algebra, Probability and Statistics -- 3 The Kalman Filter -- 4 Model Specification and Model Performance -- 5 Multivariate State Space Models -- 6 Non-linear and non-Gaussian State Space Models -- 7 The State Space Model in Finance -- 8 Dynamic Systems and Control -- References -- Index.

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.

ISBN: 9783030761240

Standard No.: 10.1007/978-3-030-76124-0doiSubjects--Topical Terms:

544465
Kalman filtering.

LC Class. No.: QA402.3 / .T75 2021

Dewey Class. No.: 519.542

Bayesian inference of state space models = Kalman filtering and beyond /
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505 0 $a 1 State Space Models -- 2 Matrix Algebra, Probability and Statistics -- 3 The Kalman Filter -- 4 Model Specification and Model Performance -- 5 Multivariate State Space Models -- 6 Non-linear and non-Gaussian State Space Models -- 7 The State Space Model in Finance -- 8 Dynamic Systems and Control -- References -- Index.
520 $a 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.
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