Hi guys,

So, I have a correlation matrix and I need to draw lognormal random numbers with correlations expressed in the matrix.

Now, we can draw correlated NORMAL distributed random numbers by applying the Cholesky decomposition and multiplying with the normal random number vektor.

But is this only possible for normal distributed random numbers, or is this method valid for all distributions, say also for Inverse Gauß or Weibull?

If not, can I go along and create uniform random numbers, correlate them and then transform them via the inverse cumulative method to say Inverse Gauß or Weibull?

A reply would be realy, realy, realy appreciated!!

Thanks in advance,

Tesla