Sentence examples for based on covariance matrix from inspiring English sources
Uncertainty levels are predicted consistently within the filter, and a discussion on observability based on covariance matrix analysis is presented.
Suitable quality criteria based on covariance matrix and bias vector are proposed as an extension of mean square criterion to multidimensional case.
Secondly, based on covariance matrix theory, CCM is proposed for calculating the background and resonant components and for compensating the cross term between background and resonant components, and the ESWLs of all components are derived by load response correlation theory.
The PCA based on covariance matrix of EcoRI/HpaII data summarized 38% of the total inertia in the two first principal components and showed pronounced differentiation between RS and SM samples.
The Principal Component Analysis (PCA) based on covariance matrix summarized 42.4% of the total inertia in the two first principal components of EcoRI/MspI data (Figure 2A), and showed very small genetic differences between RS and SM populations, with individuals from different locations being genetically very similar (i.e. central samples on Figure 2A).
Relative warp analysis is a principal component analysis based on covariance matrix where relative warps can be interpreted as principal component axes.
The paper includes a correlation analysis of the orbit parameters, Earth rotation parameters and the geopotential coefficient C20, based on covariance matrices from weekly solutions.
Statistical analysis was performed with multivariate analysis of covariance analysis, using Hotelling T(2) metric based on covariance matrices, and Pearson correlation.The OA condylar morphology was statistically significantly different from the asymptomatic condyles (P <.05).
9-10] extensively discusses the use of multivariate detectors for testing the independence of random observations with the help of the generalized likelihood ratio test (GLRT) based on covariance matrices.
The classification function based on the new covariance matrix showed a better performance compared to classification functions that were based on covariance matrices regularized by mathematical criteria along.
