Generation of discrete random variables in scalable frameworks
Abstract
In this paper, we face the problem of simulating discrete random variables with general and varying distributions in a scalable framework, where fully parallelizable operations should be preferred. The new paradigm is inspired by the context of discrete choice models. Compared to classical algorithms, we add parallelized randomness, and we leave the final simulation of the random variable to a single associative operation. We characterize the set of algorithms that work in this way, and those algorithms that may have an additive or multiplicative local noise. As a consequence, we could define a natural way to solve some popular simulation problems.
Cite
@article{arxiv.1611.07103,
title = {Generation of discrete random variables in scalable frameworks},
author = {Giacomo Aletti},
journal= {arXiv preprint arXiv:1611.07103},
year = {2018}
}
Comments
The first sections of the paper have been almost completely rewritten. A deep revision of the English has been made