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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History, Concepts, and Prospects,on and the effect of choosing a weakly informative prior are discussed. The chapter concludes by developing and implementing a sequential model monitoring technique as a means of continuously assessing model performance.

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Population Development and Regulation,ssed. Feedback control is reviewed and we present an application of vibration control for a twin rotor static rig, which can be used for air-vehicle testing. Throughout the chapter we use a number of relatively simple examples for illustration.

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Non-Linear and Non-Gaussian State Space Models,iscussing approximate inference such as the extended Kalman filter and the unscented Kalman filter. Sequential Monte Carlo methods are reviewed and some illustrative examples are presented. Markov chain Monte Carlo is discussed for the class of DGLMs, and the chapter concludes by considering dynamic survival modelling.

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Dynamic Systems and Control,ssed. Feedback control is reviewed and we present an application of vibration control for a twin rotor static rig, which can be used for air-vehicle testing. Throughout the chapter we use a number of relatively simple examples for illustration.

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Resources and Social Organisation,yesian methods. Based on sequential application of mean–variance portfolio, we discuss constrained and unconstrained portfolio selection. The chapter concludes with pairs trading, a market-neutral investment strategy, which is designed to exploit mispricing of mean-reverted spreads in order to retur

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1431-875X ve 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, in978-3-030-76126-4978-3-030-76124-0Series ISSN 1431-875X Series E-ISSN 2197-4136

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Textbook 2021 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, in

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The State Space Model in Finance,yesian methods. Based on sequential application of mean–variance portfolio, we discuss constrained and unconstrained portfolio selection. The chapter concludes with pairs trading, a market-neutral investment strategy, which is designed to exploit mispricing of mean-reverted spreads in order to retur