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# Sampling from Arbitrary Functions via PSD Models

*Proceedings of The 25th International Conference on Artificial Intelligence and Statistics*, PMLR 151:2823-2861, 2022.

#### 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