  We present an algorithm that takes a discrete random variable $X$ and a
number $m$ and computes a random variable whose support (set of possible
outcomes) is of size at most $m$ and whose Kolmogorov distance from $X$ 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.

Data Structures and Algorithms;Artificial Intelligence