Add covariance matrices?

I'm currently using / implementing this myself but I am wondering if it would make sense to add a specific type to describe covariance matrices. This is just a symmetric matrix that is guaranteed to be semi-positive definite.

Functionality (that I am currently using, but more can be added)
- Construction from either a given semi-positive definite matrix or two slices of numbers (with optional weights)
- Drawing random numbers with the given covariances (using LDL from #1515, but the existing UDU decomposition also works)
- Computing the multivariate (log) likelihood. This requires the inverse / precision matrix
- Computing the Mahalanobis distance. This also requires the inverse / precision matrix

but others could be added.

Start by reviewing the existing LDL work in issue #1515 and the UDU decomposition mentioned in the proposal. Define the scope around construction, random sampling, multivariate likelihood, precision matrices, and Mahalanobis distance; done means the covariance-matrix type supports the listed functionality for valid semi-positive definite inputs.

Written by the indexing model from the issue text.