Innovative
发表于 2025-3-25 05:57:21
https://doi.org/10.1007/978-3-030-76124-0State space models; Bayesian estimation; Financial time series; Stochastic volatility; Sequential Monte
诱拐
发表于 2025-3-25 08:10:05
978-3-030-76126-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
Fissure
发表于 2025-3-25 12:45:05
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
愤愤不平
发表于 2025-3-25 15:56:07
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
DEFT
发表于 2025-3-25 21:55:57
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
EWE
发表于 2025-3-26 02:14:35
http://reply.papertrans.cn/19/1819/181853/181853_26.png
古代
发表于 2025-3-26 08:20:59
http://reply.papertrans.cn/19/1819/181853/181853_27.png
NICHE
发表于 2025-3-26 09:20:29
http://reply.papertrans.cn/19/1819/181853/181853_28.png
数量
发表于 2025-3-26 13:54:58
http://reply.papertrans.cn/19/1819/181853/181853_29.png
阻挡
发表于 2025-3-26 19:29:27
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