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

• URL: http://arxiv.org/abs/2308.10871v1
• Date: Fri, 18 Aug 2023 08:34:45 GMT
• Title: FunQuant: A R package to perform quantization in the context of rare events and time-consuming simulations
• Authors: Charlie Sire and Yann Richet and Rodolphe Le Riche and Didier Rulli\`ere and J\'er\'emy Rohmer and Lucie Pheulpin
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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