hopsy.Gaussian.log_curvature#
- Gaussian.log_curvature(self x)#
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, which we call log_curvature in this context.
- Return type:
numpy.narray[n, n]