I did point to the spot of the example: chol_packed = pm.LKJCholeskyCov('chol_packed', n=3, eta=2, sd_dist=sd_dist).
What you’re doing now is working because you’re using theano operators to define your cov matrix. In other words, when your cov matrix is unknown and must be estimated through MCMC, you need to put a prior distribution over it. Usually it’s done in terms of a distribution of correlations matrices (LKJCorr) or a distribution of Cholesky factors (LKJCholeskyCov, which works best computationally).
I’m not sure how your parametrization will fit, as it’s quite unusual, but if it makes sense scientifically and in your use-case, you’re right to try!
The warning is theano related and you can safely ignore it for now. Also, note that using MAP is discouraged by the PyMC team – unless you know what you’re doing.