Alex wrote: > I wonder if it is possible in python to produce random numbers > according to a user defined distribution? > Unfortunately the random module does not contain the distribution I > need :-(
There exist some very general algorithms to generate random numbers from arbitrary distributions. The most notable of these are "Markov Chain Monte Carlo", e.g. the Metropolis-Hastings algorithm. It is very easy to implement in any programming language. The nature MCMC algorithms makes it inefficient when implemented in pure Python. But you can get tremendous speedup by simulating multiple Markov chains in parallel, by means of vectorizing with NumPy. A relative of Metropolis-Hastings which may also be applicable to your problem is pure "rejection sampling". It is far less efficient, but produces no autocorrelation in the samples. If you can generate random deviates from the marginal distributions, but need to sample the joint distribution, look into using the Gibbs sampler. It is an efficient version of Metropolis-Hastings for this special case. http://en.wikipedia.org/wiki/Metropolis-Hastings_algorithm http://en.wikipedia.org/wiki/Rejection_sampling http://en.wikipedia.org/wiki/Gibbs_sampling -- http://mail.python.org/mailman/listinfo/python-list
