It's the "same" method only more computationally efficient.
The first step in the method for correlation matrices is to
Cholesky decompose. So starting with the Cholesky factor is
That is, if you declare this:
L ~ lkj_corr_cholesky(5);
you get the same distribution for L * L', as you'd get for Sigma with:
Sigma ~ lkj_corr(5);
The other advantage is that you can scale the Cholesky factor once rather
than the correlation matrix and then use multi_normal_cholesky(), which
is more efficient than the multi_normal() applied to covariance matrices.
On Aug 27, 2014, at 1:55 PM, Sam Weiss <samc...@gmail.com
> Hey Ben,
> You're statement: "But basically, just declare the parameter as a cholesky_factor_cov or cholesky_factor_corr instead of a cov_matrix or corr_matrix, and it should be fine." Confuses me. My understanding was that we use this 'onion' method for large correlation / covariance matrices. But it sounds like as long as you declare "cholesky_factor_cov" we don't have to use this onion method?