Modelling and computation using NCoRM mixtures for density regression
This work addresses the need for more efficient computational methods in Bayesian nonparametric statistics, particularly for researchers in statistics and machine learning dealing with complex density regression tasks, though it appears incremental as it builds on existing normalized compound random measure frameworks.
The authors tackled the problem of building flexible nonparametric regression models for density estimation by using normalized compound random measure mixtures, and they developed a novel pseudo-marginal Metropolis-Hastings sampler for posterior inference, demonstrating its application on density regression problems.
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.