Towards a faster implementation of density estimation with logistic gaussian process priors

Published

Journal Article

A novel method is proposed to compute the Bayes estimate for a logistic Gaussian process prior for density estimation. The method gains speed by drawing samples from the posterior of a finite-dimensional surrogate prior, which is obtained by imputation of the underlying Gaussian process. We establish that imputation results in quite accurate computation. Simulation studies show that accuracy and high speed can be combined. This fact, along with known flexibility of the logistic Gaussian priors for modeling smoothness and recent results on their large support, makes these priors and the resulting density estimate very attractive. © 2007 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.

Full Text

Duke Authors

Cited Authors

  • Tokdar, ST

Published Date

  • September 1, 2007

Published In

Volume / Issue

  • 16 / 3

Start / End Page

  • 633 - 655

International Standard Serial Number (ISSN)

  • 1061-8600

Digital Object Identifier (DOI)

  • 10.1198/106186007X210206

Citation Source

  • Scopus