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arXiv · 2110.10527

Sampling from Arbitrary Functions via PSD Models

Ulysse Marteau-Ferey·Francis Bach·Alessandro Rudi
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Abstract

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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BibTeXRIS

Ulysse Marteau-Ferey, Francis Bach, Alessandro Rudi. 2021-10-20. Sampling from Arbitrary Functions via PSD Models. https://arxiv.org/abs/2110.10527

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