000			02706nam a22002895i 4500
001			92372
005			20231110185458.0
010			_a978-3-030-76124-0 _dcompra
090			_a92372
100			_a20231023d2021 k||y0pory50 ba
101	0		_aeng
102			_aCH
200	1		_aBayesian inference of state space models _bDocumento eletrónico _eKalman filtering and beyond _fby Kostas Triantafyllopoulos
210			_aCham _cSpringer International Publishing _d2021
215			_aXV, 495 p. _cil.
225	2		_aSpringer texts in statistics
303			_aBayesian 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.
606			_aStatistics
606			_aSystem theory
606			_aControl theory
680			_aQA276-280
700			_970578 _aTriantafyllopoulos _bKostas
801		0	_aPT _gRPC
856	4		_uhttps://doi.org/10.1007/978-3-030-76124-0
942			_2lcc _cF _n0