3 ms·
Correct, that's only useful to check for degeneracy. The test is much more certain. And it's bigger than 2x2, it depends on number of dimensions you're checkin
by AstralStorm 7y ago
Correct, that's only useful to check for degeneracy. The test is much more certain.
And it's bigger than 2x2, it depends on number of dimensions you're checking. 3 variables goes 3x3 etc.
For fitting as shown (and GANs), it gets big really fast.
- kgwgk 7y agoI don't understand what covariance matrix you're talking about. Let's say we have two variables and the joint distribution is given by the probability density function p(x,y) which can have any form as long as it's always >=0 and integrates to 1. The covariance matrix is a 2x2 matrix and contains only three real numbers (four, but the off-diagonal terms are necessarily equal). The covariance matrix (together with the vector of means) gives a complete description of the joint distribution only when the joint distribution is completely specified by its first two moments and all its higher moments are zero (i.e. it is a multivariate normal). The fact that the correlation between the variables is zero doesn't mean necessarily that the variables are independent when the joint distribution is not a multivariate normal. The covariance matrix alone cannot be used to tell whether the joint distribution is a multivariate normal or not. Every possible covariance matrix defines a multivariate normal but is also compatible with an infinity of other distributions.