Bayesian Inference of State Space Models: Kalman Filtering and Beyond (Springer Texts in Statistics) free ebook download.

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Bayesian Inference of State Space Models: Kalman Filtering and Beyond (Springer Texts in Statistics) free ebook download

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Author(s):	Kostas Triantafyllopoulos

Date:	2021	Format:	7z	Language:	English	ISBN/ASIN:	3030761231
Pages:	510	OCR:		Quality:		ISBN13:
Uploader:	alex21s	Upload Date:	11/21/2021 8:46:46 PM

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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.
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Tags: Bayesian Beyond Filtering Inference Kalman Models Space Springer State Statistics

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