Since this question has garnered a lot of views, I thought I'd post code for the final answer that I found, in part, by posting to the Eigen forums . The code uses Boost for the univariate normal and Eigen for matrix handling. It feels rather unorthodox, since it involves using the "internal" namespace, but it works.
I'm open to improving it if someone suggests a way.
Note that this does not require you to compute the Cholskey factorization. Although, I think SVD is slower than Cholskey, but they must both be cubic in number of flops.
I cant really gove you an answer,but what I can give you is a way to a solution, that is you have to find the anglde that you relate to or peaks your interest. A good paper is one that people get drawn into because it reaches them ln some way.As for me WW11 to me, I think of the holocaust and the effect it had on the survivors, their families and those who stood by and did nothing until it was too late.