Sampling from Arbitrary Functions via PSD Models
Abstract
In many areas of applied statistics and machine learning, generating an arbitrary number of inde- pendent 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 implemen- tations. 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 e
Cite
Text
Marteau-Ferey et al. "Sampling from Arbitrary Functions via PSD Models." Artificial Intelligence and Statistics, 2022.Markdown
[Marteau-Ferey et al. "Sampling from Arbitrary Functions via PSD Models." Artificial Intelligence and Statistics, 2022.](https://mlanthology.org/aistats/2022/marteauferey2022aistats-sampling/)BibTeX
@inproceedings{marteauferey2022aistats-sampling,
title = {{Sampling from Arbitrary Functions via PSD Models}},
author = {Marteau-Ferey, Ulysse and Bach, Francis and Rudi, Alessandro},
booktitle = {Artificial Intelligence and Statistics},
year = {2022},
pages = {2823-2861},
volume = {151},
url = {https://mlanthology.org/aistats/2022/marteauferey2022aistats-sampling/}
}