Probabilistic Transforms for Combinatorial Urn Models

In this paper, we present several probabilistic transforms related to classical urn models. These transforms render the dependent random variables describing the urn occupancies into independent random variables with appropriate distributions. This simplifies the analysis of a large number of problems for which a function under investigation depends on the urn occupancies. The approach used for constructing the transforms involves generating functions of combinatorial numbers. We also show, by using Tauberian theorems derived in this paper, that under certain conditions the asymptotic expressions of target functions in the transform domain are identical to the asymptotic expressions in the inverse–transform domain. Therefore, asymptotic information about certain statistics can be gained without evaluating the inverse transform.