We demonstrate an algorithm for generating random vectors based on a sample of observations of a multivariate random variable X. Although our goal is the generation of pseudo observations which behave as though they came from the underlying density of X, in the algorithm discussed, estimation of the underlying density is not required. Rather, the observations are combined using stochastic multipliers to generate simulated observations. Because of the local nature of the generation scheme, the algorithm requires few assumptions on the underlying density. The algorithm is intended for use in simulation studies in which a large number of nonrepeated pseudo observations are to be obtained from a relatively small set of experimental data.