Knowledge of the process noise covariance matrix is essential for the application of Kalman filtering. However, it is usually a difficult task to obtain an explicit expression of for large time varying systems. This paper looks at an adaptive

Approach based on covariance matrix Approach based on covariance matrix Approach based on covariance matrix Approach based on covariance matrix-Approach based on covariance matrix Approach based on covariance matrix Approach based on covariance matri

Using SAS/IML : This code uses the EM algorithm to estimate the maximum likelihood (ML) covariance matrix and mean vector in the presence of missing data. This implementation of the EM algorithm or any similar ML approach assumes that the data are

一种基于贝叶斯框架的线性分类。使用神经生理学信息和实验信息构建协方差矩阵。-A linear classification based on Bayesian framework. Covariance matrix is ​ ​ constructed using information and experimental neurophysiology information.