hopsy.Gaussian.compute_expected_fisher_information

Gaussian.compute_expected_fisher_information(self, x)

deprecated:: 1.4

Use log_curvature() instead.

Computes the expected fisher information of a multivariate Gaussian model in \(n-k\) dimensions at x. This turns out to be just the reduced covariance matrix. Note that x still has to have dimension \(n\).

Parameters:

x (numpy.ndarray[n, 1]) – Input vector

Returns:

The expected Fisher information matrix

Return type:

numpy.narray[n, n]