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correlation matrix वाक्य

"correlation matrix" हिंदी मेंcorrelation matrix in a sentence
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  • Similarly, it is possible to update the Cholesky factor when a row or column is added, without creating the inverse of the correlation matrix explicitly.
  • However, mean-centering is unnecessary in any regression analysis, as one uses a correlation matrix and the data are already centered after calculating correlations.
  • One strategy is to define a correlation matrix " A " which is then multiplied by a scalar to give a covariance matrix : this must be positive definite.
  • For this reason, some of the methods used in the analyses of the correlation matrix ( e . g . the PCA ) have to be replaced or are less efficient.
  • Since this soft-thresholding procedure applied to a pairwise correlation matrix leads to weighted adjacency matrix, the ensuing analysis is referred to as weighted gene co-expression network analysis.
  • "' Image factoring "'is based on the correlation matrix of predicted variables rather than actual variables, where each variable is predicted from the others using multiple regression.
  • Where K is as above and \ bar r the mean of the K ( K-1 ) / 2 non-redundant upper triangular, or lower triangular, correlation matrix ).
  • The estimation of MVAR coefficients is based on calculation of the correlation matrix " R ij " of " k " signals " X i " from multivariate set.
  • Equivalently, the correlation matrix can be seen as the covariance matrix of the standardized random variables X _ i / \ sigma ( X _ i ) for i = 1, \ dots, n.
  • In terms of the correlation matrix, this corresponds with focusing on explaining the off-diagonal terms ( i . e . shared co-variance ), while PCA focuses on explaining the terms that sit on the diagonal.
  • Before the advent of high speed computers, considerable effort was devoted to finding approximate solutions to the problem, particularly in estimating the communalities by other means, which then simplifies the problem considerably by yielding a known reduced correlation matrix.
  • If the measures of correlation used are product-moment coefficients, the correlation matrix is the same as the covariance matrix of the positive definite if no variable can have all its values exactly generated as a linear combination of the others.
  • Correlation coefficients are calculated between the six time-series and the principal components are the eigenvectors of the resulting correlation matrix . ( Empirical orthogonal function analysis proceeds instead from the variance-covariance matrix which again is biased to the eastern longitudes .)
  • In practice, typically a single correlation parameter ( not a correlation matrix ) is used to model the default correlation between any two entities in a collateralized debt obligation, CDO . Conceptually this correlation parameter should be the same for the entire CDO portfolio.
  • Note that the node-node correlations ( or for simplicity the node correlations ) for all pairs of nodes define a symmetric correlation matrix whose ( i, j ) element is the correlation between nodes " i " and " j ".
  • In probability and statistics, the term "'cross-correlations "'is used for referring to the correlations between the entries of " X " itself, those forming the correlation matrix ( matrix of correlations ) of " X ".
  • For example, the unique optimal whitening transformation achieving maximal component-wise correlation between original X and whitened Y is produced by the whitening matrix W = P ^ {-1 / 2 } V ^ {-1 / 2 } where P is the correlation matrix and V the variance matrix.
  • A special case of generalized least squares called "'weighted least squares "'occurs when all the off-diagonal entries of " ? " ( the correlation matrix of the residuals ) are null; the variances of the observations ( along the covariance matrix diagonal ) may still be unequal ( heteroscedasticity ).
  • For the cases where has full row or column rank, and the inverse of the correlation matrix ( AA ^ * for with full row rank or A ^ * A for full column rank ) is already known, the pseudoinverse for matrices related to A can be computed by applying the Sherman Morrison Woodbury formula to update the inverse of the correlation matrix, which may need less work.
  • For the cases where has full row or column rank, and the inverse of the correlation matrix ( AA ^ * for with full row rank or A ^ * A for full column rank ) is already known, the pseudoinverse for matrices related to A can be computed by applying the Sherman Morrison Woodbury formula to update the inverse of the correlation matrix, which may need less work.
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