Lines Matching refs:covariance

2157 non-linear least squares solve is to analyze the covariance of the
2166 covariance. Then the maximum likelihood estimate of :math:`x` given
2172 And the covariance of :math:`x^*` is given by
2179 If :math:`J(x^*)` is rank deficient, then the covariance matrix :math:`C(x^*)`
2184 Note that in the above, we assumed that the covariance matrix for
2191 covariance of :math:`y`, then the maximum likelihood problem to be
2196 and the corresponding covariance estimate of :math:`x^*` is given by
2201 covariance matrix not equal to identity, then it is the user's
2205 where :math:`S^{-1/2}` is the inverse square root of the covariance
2222 :class:`Covariance` allows the user to evaluate the covariance for a
2225 residuals such that their covariance is identity.
2227 Since the computation of the covariance matrix requires computing the
2230 user is only interested in a small part of the covariance
2232 allows the user to specify the parts of the covariance matrix that she
2234 store those parts of the covariance matrix.
2281    estimation of covariance.
2288    Ceres supports three different algorithms for covariance
2321    computing the covariance if the Jacobian is rank deficient.
2372     with singular and near singular covariance matrices.
2374     As mentioned above, when the covariance matrix is near singular,
2412    function and in turn its effect on the covariance.
2417    the covariance estimation algorithm. Covariance estimation is a
2424    Compute a part of the covariance matrix.
2426    The vector ``covariance_blocks``, indexes into the covariance
2428    covariance estimation algorithm to only compute and store these
2431    Since the covariance matrix is symmetric, if the user passes
2439    determine what parts of the covariance matrix are computed. The
2443    The return value indicates the success or failure of the covariance
2450    Return the block of the covariance matrix corresponding to
2461    returned covariance will be a row-major matrix.
2478  Covariance covariance(options);
2485  CHECK(covariance.Compute(covariance_blocks, &problem));
2490  covariance.GetCovarianceBlock(x, x, covariance_xx)
2491  covariance.GetCovarianceBlock(y, y, covariance_yy)
2492  covariance.GetCovarianceBlock(x, y, covariance_xy)