Lines Matching full:matrix
84 // If J(x*) is rank deficient, then the covariance matrix C(x*) is
90 // matrix for y was identity. This is an important assumption. If this
95 // Where S is a positive semi-definite matrix denoting the covariance
105 // covariance matrix not equal to identity, then it is the user's
109 // is the inverse square root of the covariance matrix S.
116 // Since the computation of the covariance matrix requires computing
117 // the inverse of a potentially large matrix, this can involve a
120 // covariance matrix. Quite often just the block diagonal. This class
121 // allows the user to specify the parts of the covariance matrix that
123 // and store those parts of the covariance matrix.
139 // Numerical rank deficiency, where the rank of the matrix cannot be
250 // If the Jacobian matrix is near singular, then inverting J'J
256 // which is essentially a rank deficient matrix, we have
286 // As mentioned above, when the covariance matrix is near
306 // truncated matrix is still below
332 // Compute a part of the covariance matrix.
334 // The vector covariance_blocks, indexes into the covariance matrix
339 // Since the covariance matrix is symmetric, if the user passes
347 // what parts of the covariance matrix are computed. The full
359 // Return the block of the covariance matrix corresponding to
370 // parameter_block1_size x parameter_block2_size matrix. The
371 // returned covariance will be a row-major matrix.
