> > Question still is. If I have different random variables, should I use the > same random number generator or should I have a new one for every random > variable. If I should use a new one. How do I obtain the deterministic > sequence of initial seeds such that the different generators produce good > independent sequences? >
You do not need different RNGs for differing random variables. For example, you can generate from a normal distribution and from a gamma distribution using the same RNG. For instance, people working with Bayesian statistics run a Markov chain Monte Carlo algorithm, such as the Gibbs sampler, with a single RNG (eg. gsl_rng_mt19937). In that algorithm, you may sample from different conditional distributions (normal, gamma, beta, multivariate normal, Student-t, Wishart, etc) using the same RNG. That being said, if you use GSL, then initialize your RNG and use it in your whole program. (The same happens in R and Matlab.) Thus, you only need to save one RNG SEED. Ralph. _______________________________________________ Help-gsl mailing list Help-gsl@gnu.org http://lists.gnu.org/mailman/listinfo/help-gsl
