| 期刊全称 | Bayesian Inference of State Space Models | | 期刊简称 | Kalman Filtering and | | 影响因子2023 | Kostas Triantafyllopoulos | | 视频video | | | 发行地址 | Provides a comprehensive account of linear and non-linear state space modelling, including R.Discusses in detail the applications to financial time series, dynamic systems, and control.Reviews simulat | | 学科分类 | Springer Texts in Statistics | | 图书封面 |  | | 影响因子 | .Bayesian 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, in | | Pindex | Textbook 2021 |
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发表于 2025-3-21 16:40:17
