FunQuant: A R package to perform quantization in the context of rare events and time-consuming simulations - Archive ouverte HAL Accéder directement au contenu
Pré-Publication, Document De Travail (Preprint/Prepublication) Année : 2023

FunQuant: A R package to perform quantization in the context of rare events and time-consuming simulations

Résumé

Quantization summarizes continuous distributions by calculating a discrete approximation. Among the widely adopted methods for data quantization is Lloyd's algorithm, which partitions the space into Vorono\"i cells, that can be seen as clusters, and constructs a discrete distribution based on their centroids and probabilistic masses. Lloyd's algorithm estimates the optimal centroids in a minimal expected distance sense, but this approach poses significant challenges in scenarios where data evaluation is costly, and relates to rare events. Then, the single cluster associated to no event takes the majority of the probability mass. In this context, a metamodel is required and adapted sampling methods are necessary to increase the precision of the computations on the rare clusters.

Dates et versions

hal-04189822 , version 1 (29-08-2023)

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Charlie Sire, Yann Richet, Rodolphe Le Riche, Didier Rullière, Jérémy Rohmer, et al.. FunQuant: A R package to perform quantization in the context of rare events and time-consuming simulations. 2023. ⟨hal-04189822⟩
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