An optimal approximation of discrete random variables with respect to
the Kolmogorov distance
Abstract
We present an algorithm that takes a discrete random variable and a number and computes a random variable whose support (set of possible outcomes) is of size at most and whose Kolmogorov distance from is minimal. In addition to a formal theoretical analysis of the correctness and of the computational complexity of the algorithm, we present a detailed empirical evaluation that shows how the proposed approach performs in practice in different applications and domains.
View on arXivComments on this paper
