pith. sign in

arxiv: 1805.07535 · v1 · pith:ZVPUO35Fnew · submitted 2018-05-19 · 💻 cs.DS · cs.AI

An optimal approximation of discrete random variables with respect to the Kolmogorov distance

classification 💻 cs.DS cs.AI
keywords randomalgorithmdiscretedistancekolmogorovvariablewhoseaddition
0
0 comments X
read the original abstract

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.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.