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The Gaussian process density sampler

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Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 21: 22nd Annual Conference on Neural Information Processing Systems 2008
EditorsD Koller
PublisherCurran Associates Inc
Pages9-16
Number of pages8
ISBN (Print)978-1-60560-949-2
Publication statusPublished - 2009

Abstract

We present the Gaussian Process Density Sampler (GPDS), an exchangeable generative model for use in nonparametric Bayesian density estimation. Samples drawn from the GPDS are consistent with exact, independent samples from a fixed density function that is a transformation of a function drawn from a Gaussian process prior. Our formulation allows us to infer an unknown density from data using Markov chain Monte Carlo, which gives samples from the posterior distribution over density functions and from the predictive distribution on data space. We can also infer the hyperparameters of the Gaussian process. We compare this density modeling technique to several existing techniques on a toy problem and a skull-reconstruction task.

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