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Nonparametric Bayesian Density Modeling with Gaussian Processes

Adams, RP and Murray, I and MacKay, DJC (2009) Nonparametric Bayesian Density Modeling with Gaussian Processes.

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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 distribution defined by a density 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 describe two such MCMC methods. Both methods also allow inference of the hyperparameters of the Gaussian process.

Item Type: Article
Uncontrolled Keywords: stat.CO stat.CO math.ST stat.TH
Subjects: UNSPECIFIED
Divisions: Div F > Machine Intelligence
Depositing User: Cron Job
Date Deposited: 07 Mar 2014 11:59
Last Modified: 18 Aug 2014 01:03
DOI:

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