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Pré-Publication, Document De Travail Année : 2021

Sampling from Arbitrary Functions via PSD Models

Résumé

In many areas of applied statistics and machine learning, generating an arbitrary number of independent and identically distributed (i.i.d.) samples from a given distribution is a key task. When the distribution is known only through evaluations of the density, current methods either scale badly with the dimension or require very involved implementations. Instead, we take a two-step approach by first modeling the probability distribution and then sampling from that model. We use the recently introduced class of positive semi-definite (PSD) models, which have been shown to be efficient for approximating probability densities. We show that these models can approximate a large class of densities concisely using few evaluations, and present a simple algorithm to effectively sample from these models. We also present preliminary empirical results to illustrate our assertions.
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Dates et versions

hal-03386544 , version 1 (19-10-2021)
hal-03386544 , version 2 (27-10-2021)
hal-03386544 , version 3 (23-02-2022)

Identifiants

Citer

Ulysse Marteau-Ferey, Francis Bach, Alessandro Rudi. Sampling from Arbitrary Functions via PSD Models. 2021. ⟨hal-03386544v2⟩
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