Am 18.04.2011 um 14:53 schrieb X Statistics: > 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. > > Thanks for the help. This was all I wanted to know.
Toralf Niebuhr _______________________________________________ Help-gsl mailing list Help-gsl@gnu.org http://lists.gnu.org/mailman/listinfo/help-gsl
