Exact(15)
We propose a modified Tikhonov regularization method for obtaining a regularized solution.
Here, a hybrid regularization method is used for computing a regularized solution.
It is a convenient graphical tool for displaying the trade-off between the size of a regularized solution and its fit to the given data, as the regularization parameter varies.
After reconstruction of the grey matter surface from an individual's high-resolution T1-weighted MRI, we specify a set of anatomically informed basis functions, fit the model parameters for a single time point, using a regularized solution, and finally make inferences about the estimated parameters over time.
Let be a regularized solution defined by (2.4), with data.
Then the difference between and a regularized solution can be estimated: (2.29).
Similar(45)
However, the a priori bound E cannot be known exactly in practice, and using a wrong constant E may lead to a badly regularized solution.
However, the a priori bound E cannot be known exactly in practice, and working with a wrong constant E may lead to a badly regularized solution.
We find strong evidence that improved spatial resolution with fixed blob size leads to a converged, regularized solution without numerical instabilities.
Levenberg-Marquardt minimization with Bayesian regularization is also implemented, providing an optimal regularized solution and insight into parametrization efficiency.
In theoretical results, we have suggested a general filter regularization method of regularized solution (Section 2.2).
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