Functional regression approximate Bayesian computation for Gaussian process density estimation
Computation
2014-10-31 v1
Abstract
We propose a novel Bayesian nonparametric method for hierarchical modelling on a set of related density functions, where grouped data in the form of samples from each density function are available. Borrowing strength across the groups is a major challenge in this context. To address this problem, we introduce a hierarchically structured prior, defined over a set of univariate density functions, using convenient transformations of Gaussian processes. Inference is performed through approximate Bayesian computation (ABC), via a novel functional regression adjustment. The performance of the proposed method is illustrated via a simulation study and an analysis of rural high school exam performance in Brazil.
Cite
@article{arxiv.1410.8276,
title = {Functional regression approximate Bayesian computation for Gaussian process density estimation},
author = {G. S. Rodrigues and David J. Nott and S. A. Sisson},
journal= {arXiv preprint arXiv:1410.8276},
year = {2014}
}