Sentence examples for matrix of spatial from inspiring English sources

The SAR error model is defined as Y = Xβ + λ Wu + e and the SAR lagged model as Y = ρ WY + Xβ + e where λ and ρ are the spatial autoregression coefficients, u is the spatially dependent error term, and W is the matrix of spatial weights (Fortin and Dale 2005; Bivand et al. 2008).

They represent the installation of biological and molecular function within the matrix of spatial regulatory states.

The knowledge of the exact dynamic stiffness matrix of the single arch makes the direct evaluation of the exact global dynamic stiffness matrix of spatial arch structures possible.

W is the n × n matrix of spatial weights for each period of t = 1 before the training and t = 2 after the training, (Y_{it}) is the n × 1 column vector of the dependent variable (test scores), and X it is the n × k matrix of regressors.

Consider the space-time occurrence rate conditioned on the occurrence history H t up to time t such that (2 where (x j, y j ) and S j are the aftershock centroid and normalized variance-covariance matrix of spatial clusters, respectively, which are specified in the next section.

E ε i 2 = σ 2 h z i. E[ε i ε j ] = 0 with i ≠ j; Where: y is i × 1 vector of observations HNV farmland indicator; X is the i × k matrix of the k determinants of the HNV farmland indicator; β is the vector of regression parameters (to be estimated); ε is the error term; W i is the i × i matrix of spatial weights; ρ and λ are the spatial lag parameter and the spatial error coefficient, respectively.

The DI can be automatically computed at the sensor as well as at the source level (Fig. 2) on a large matrix of spatial-temporal data, without having to select particular peaks, or components.

In this work, we implemented the DI because it allows to quantify the differences between TMS-evoked potentials starting directly from the entire matrix of spatial-temporal data, without requiring a priori information, and to synthesize them into a single number.

We considered three factors that may affect the performance of the algorithm, that is, the cut-off threshold applied to FET result for switching FDR (or p values) to the binary observed state (only for FHB), the transition matrix (degree of spatial dependency) used to generate the ground truth, and the sequencing depth (library size) of the data.

With the polynomial projection model, we will briefly analyze the projected image and the channel matrix of the spatial-multiplexing VLC system.

Conventional algorithms used for parameter estimation in colocated multiple-input-multiple-output (MIMO) radars require the inversion of the covariance matrix of the received spatial samples.