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In many areas of applied statistics and machine learning, generating anarbitrary number of independent and identically distributed (i.i.d.) samplesfrom a given distribution is a key task. When the distribution is known onlythrough evaluations of the density, current methods either scale badly with thedimension or require very involved implementations. Instead, we take a two-stepapproach by first modeling the probability distribution and then sampling fromthat model. We use the recently introduced class of positive semi-definite(PSD) models, which have been shown to be efficient for approximatingprobability densities. We show that these models can approximate a large classof densities concisely using few evaluations, and present a simple algorithm toeffectively sample from these models. We also present preliminary empiricalresults to illustrate our assertions.

Sampling from Arbitrary Functions via PSD Models