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To simulate expression matrices, we assumed two different models: 1. Coherent model.
To generate matrices, we assumed three types of aberrations: concordant true non-concordant true and concordant false positive aberrations.
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For the a priori covariance matrix, we assumed a diagonal matrix whose elements are the observational error of 0.13 km/s based on phase velocity measurements on the similar ray paths.
For the test-retest correlation matrix, we assumed that it followed a generalization of an autocorrelation (AR) form called a LEAR model [ 53].
Suppose (Xinmathbb {R}^{ntimes n}) is an arbitrary symmetric matrix, we assume that the eigenvalues of X are arranged so that (lambda_{1}(X geqcdotsgeqlambdaeqcdotsgeqlambda _{n}(X)).
Given the co-occurrence of "heritability" with "variation" in the 2011 postunit word association matrix, we assume that students somehow relate the two concepts.
Let M be a complex n × n matrix; we assume in fact that M belongs to the Siegel half-space Σ n + = { M : M = M T, Im M > 0 }.
In our experiments, we did not account for the time required to build these matrices since we assumed that it amortizes with the large number of variables and time steps present in the data.
By decomposing the multiplication DA into the sum of K rank 1 matrices, we can assume that the other K−1 terms were fixed, and the kth remains unknown.
Concerning the matrix A we assume that A is symmetric, positive definite and that its entries are C1,γ-smooth functions.
(9)Notice that the system response A here is an N × 4 complex matrix; so we assume that N > 4 and ensure rank(A) = 4 to estimate four-dimensional complex vector X t [3, 5].
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