Towards a faster implementation of density estimation with logistic gaussian process priors
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.
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- Statistics & Probability
- 1403 Econometrics
- 0104 Statistics
Citation
Published In
DOI
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Statistics & Probability
- 1403 Econometrics
- 0104 Statistics