標(biāo)題: Titlebook: Bayesian Inference of State Space Models; Kalman Filtering and Kostas Triantafyllopoulos Textbook 2021 The Editor(s) (if applicable) and Th [打印本頁] 作者: 積聚 時(shí)間: 2025-3-21 16:40
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作者: Stress-Fracture 時(shí)間: 2025-3-21 20:29 作者: 陳腐思想 時(shí)間: 2025-3-22 03:36
History, Concepts, and Prospects,ion 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 brief historical overview of the developments that led to the discovery of the Kalman filter and the research directions in the wider field of state space modelling.作者: Conducive 時(shí)間: 2025-3-22 06:55
History, Concepts, and Prospects,g Bayesian conjugate methods, EM algorithm and Markov chain Monte Carlo methods. In particular, the forward filtering backward sampling is discussed in detail. A data set consisting of daily values of several pollutants is used to illustrate estimation and forecasting.作者: 大猩猩 時(shí)間: 2025-3-22 10:39 作者: 碎片 時(shí)間: 2025-3-22 16:46 作者: 音樂戲劇 時(shí)間: 2025-3-22 17:37 作者: 細(xì)菌等 時(shí)間: 2025-3-23 01:14 作者: gait-cycle 時(shí)間: 2025-3-23 03:22
Matrix Algebra, Probability and Statistics,stributions are briefly discussed. In a similar fashion we set the statistics background, with a focus on Bayesian inference. Some statistical topics such as Markov chain Monte Carlo and particle filters are introduced in later chapters.作者: Medicare 時(shí)間: 2025-3-23 08:24
The Kalman Filter, fixed-interval smoothing and covariance smoothing is discussed in some detail. The chapter concludes by considering the steady state of the Kalman filter, motivated by considering the random walk plus noise model.作者: Vulnerary 時(shí)間: 2025-3-23 13:36
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.作者: rectum 時(shí)間: 2025-3-23 17:45 作者: canonical 時(shí)間: 2025-3-23 21:06
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.作者: 神圣在玷污 時(shí)間: 2025-3-23 22:22 作者: 你不公正 時(shí)間: 2025-3-24 03:30
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.作者: 遷移 時(shí)間: 2025-3-24 09:22
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.作者: CORD 時(shí)間: 2025-3-24 11:07
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作者: misanthrope 時(shí)間: 2025-3-24 18:33
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 作者: Anticoagulants 時(shí)間: 2025-3-24 19:40
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作者: vascular 時(shí)間: 2025-3-25 03:08
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作者: Innovative 時(shí)間: 2025-3-25 05:57
https://doi.org/10.1007/978-3-030-76124-0State space models; Bayesian estimation; Financial time series; Stochastic volatility; Sequential Monte 作者: 誘拐 時(shí)間: 2025-3-25 08:10
978-3-030-76126-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: Fissure 時(shí)間: 2025-3-25 12:45
History, Concepts, and Prospects,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作者: 憤憤不平 時(shí)間: 2025-3-25 15:56
https://doi.org/10.1007/3-540-31391-5d 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作者: DEFT 時(shí)間: 2025-3-25 21:55
https://doi.org/10.1007/3-540-31391-5he celebrated Kalman filter. We present two proofs of the popular filter, one based on multivariate distribution theory and one based on minimising the error covariance matrix. We briefly describe the package ‘BTSA’ available within the programming language R, which is used throughout the book for f作者: EWE 時(shí)間: 2025-3-26 02:14 作者: 古代 時(shí)間: 2025-3-26 08:20 作者: NICHE 時(shí)間: 2025-3-26 09:20 作者: 數(shù)量 時(shí)間: 2025-3-26 13:54 作者: 阻擋 時(shí)間: 2025-3-26 19:29
Population Development and Regulation,ribe and expand on the origins of the Kalman filter and to provide some insights by bringing together scientists of different disciplines working on similar methods. The chapter first defines dynamic systems and then focuses on linear systems. The state space representation of a system is discussed 作者: 承認(rèn) 時(shí)間: 2025-3-26 21:32 作者: cacophony 時(shí)間: 2025-3-27 02:07
Springer Texts in Statisticshttp://image.papertrans.cn/b/image/181853.jpg作者: cortex 時(shí)間: 2025-3-27 06:09
Bayesian Inference of State Space Models978-3-030-76124-0Series ISSN 1431-875X Series E-ISSN 2197-4136 作者: watertight, 時(shí)間: 2025-3-27 12:52
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作者: 吹氣 時(shí)間: 2025-3-27 14:28
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作者: 令人作嘔 時(shí)間: 2025-3-27 20:51 作者: –DOX 時(shí)間: 2025-3-27 23:10 作者: 種子 時(shí)間: 2025-3-28 03:31 作者: Substance-Abuse 時(shí)間: 2025-3-28 09:38
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作者: Baffle 時(shí)間: 2025-3-28 11:08
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作者: jealousy 時(shí)間: 2025-3-28 17:10
Dynamic Systems and Control,ribe and expand on the origins of the Kalman filter and to provide some insights by bringing together scientists of different disciplines working on similar methods. The chapter first defines dynamic systems and then focuses on linear systems. The state space representation of a system is discussed
