Evolution strategies for multivariate-to-anything partially specified random vector generation

Abstract

Multivariate-to-anything methods for partially specified random vector generation work by transforming samples from a driving distribution into samples characterized by given marginals and correlations. The correlations of the transformed random vector are controlled by the driving distribution; sampling a partially specified random vector requires finding an appropriate driving distribution. This paper motivates the use of evolution strategies for solving such problems and compares evolution strategies to conjugate gradient methods in the context of solving a Dirichlet-to-anything transformation. It is shown that the evolution strategy is at least as effective as the conjugate gradient method for solution of the parameterization problem.