Titlebook: Bayesian Inference of State Space Models; Kalman Filtering and Kostas Triantafyllopoulos Textbook 2021 The Editor(s) (if applicable) and Th

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Springer Texts in Statisticshttp://image.papertrans.cn/b/image/181853.jpg

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Bayesian Inference of State Space Models978-3-030-76124-0Series ISSN 1431-875X Series E-ISSN 2197-4136

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State Space Models,Examples include linear trend and seasonal time series, time-varying regression, bearings-only tracking, financial time series and systems identification state space models. The chapter sets the stage for the book and provides a chapter-by-chapter description of the book. The chapter includes a brie

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Matrix Algebra, Probability and Statistics,d statistics. Because linear models in particular depend heavily on matrices, it deemed necessary to review some topics of matrix analysis, such as matrix differentiation. Rather than just stating results, which can be found in the literature, for pedagogical reasons we develop some of the arguments

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Non-Linear and Non-Gaussian State Space Models,n-Gaussian and non-linear state space models. The text reviews some classes of the many possibilities of non-Gaussian models. In particular, dynamic generalised linear models (DGLM) are discussed aimed at categorical time series, count data, data for positive-valued time series, continuous proportio

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The State Space Model in Finance,he state space model can be used in this class of models. Stationarity has played an important role historically in economics and econometrics. Here we review the basic principles of stationarity and we provide an alternative proof for the stationarity conditions of autoregressive models of order th