How to incorporate random effect which follows multivariate Gaussian distribution in Linear-Mixed-Effect model?

I have a linear mixed effect model as follows:

y_ij=X_ij*b+Z_ij*B_i .

Here B_i~MGP(0,C). Now how can I incorporate this parameterization of covariance matrix into the model? As the model only allows log-Cholesky,full-Cholesky, diagonal etc. parameterization of the matrix C. Also Z here is B-spline basis matrix.

[Bernhard Suhm]

Can you clarify what you are referring to with MGP(0,C)? The doc for LinearMixedModel mentions a couple other covariance matrix types than log/full Cholesky, but I assume above is not one of those.

[Mithun Ghosh]

Actually I have n-population so my random effect coefficient is following n-gaussian process which is multivariate gaussian process(MGP).Thus I want to parameterize covatiance matrix according to MGP where C is my multivarite covariance kernel.