Nonparametric Bayesian Density Modeling with Gaussian Processes.
Fri, 12/25/2009 - 16:01 — SomNambul
link: http://arxiv.org/abs/0912.4896
Abstract
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.
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