ENH: stats.covariance: add CovViaCholesky

[mdhaber]

Reference issue

Followup to gh-16002

What does this implement/fix?

gh-16002 added the ability to specify a covariance matrix via its inverse.
This PR adds the ability to specify a diagonal covariance matrix via its Cholesky decomposition.

[tupui]

LGTM, thanks Matt 👍

Do we plan to add more decomposition options?

[mdhaber]

Do we plan to add more decomposition options?

I have two more on deck (that I've already written): eigendecomposition and LDL. LDL is the one that should be the default if we want to be able to instantiate a Covariance object directly because it is faster than eigendecomposition and more robust than Cholesky. Eigendecomposition is important because it's probably the most accurate. It would be easy to add others - basically, any matrix that has special structure for which we have a dedicated solver (e.g. solveh_banded, solve_toeplitz) could be useful. It may also be useful to add a sparse option, and we could add something for rotation matrices to satisfy gh-15675. But I can let others have fun with these; I'm just paving the way and adding the ones I think are most fundamental.

After that, @siddhantwahal has wanted (for a long time, see gh-11772) for rvs to re-use the decomposition. So if the user provides a Covariance object, we'll use it (instead of np.multivariate_normal) to generate random variates. You just take independent, standard normal random variate and color them (inverse of whiten).

Also, with a few modifications, these classes/multivariate_normal can broadcast over ND stacks of covariance matrices (and ND mean). This is something that @OriolAbril wanted to see for ArviZ/PyMC. I already made the required changes and wrote tests in my own branch; it's just a matter of putting them in a reasonably-sized PR. (All of these have essentially been copy-pasted from mdhaber#88, which is why they are coming one after the other.)

[tupui]

That's all great. I also just realizing that this work can also help qmc.MultivariateNormalQMC.