Generation of discrete random variables in scalable frameworks
This work addresses the need for efficient simulation methods in scalable computing environments, offering a novel approach that could improve performance in applications requiring discrete random variable generation.
The paper tackles the problem of simulating discrete random variables with varying distributions in scalable frameworks by introducing a new paradigm inspired by discrete choice models, resulting in a characterization of algorithms that use parallelized randomness and a single associative operation for final simulation.
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.