Modelling and computation using NCoRM mixtures for density regression
Methodology
2017-09-01 v3 Applications
Computation
Machine Learning
Abstract
Normalized compound random measures are flexible nonparametric priors for related distributions. We consider building general nonparametric regression models using normalized compound random measure mixture models. Posterior inference is made using a novel pseudo-marginal Metropolis-Hastings sampler for normalized compound random measure mixture models. The algorithm makes use of a new general approach to the unbiased estimation of Laplace functionals of compound random measures (which includes completely random measures as a special case). The approach is illustrated on problems of density regression.
Cite
@article{arxiv.1608.00874,
title = {Modelling and computation using NCoRM mixtures for density regression},
author = {Jim Griffin and Fabrizio Leisen},
journal= {arXiv preprint arXiv:1608.00874},
year = {2017}
}