  We introduce a random partition model for Bayesian nonparametric regression.
The model is based on infinitely-many disjoint regions of the range of a latent
covariate-dependent Gaussian process. Given a realization of the process, the
cluster of dependent variable responses that share a common region are assumed
to arise from the same distribution. Also, the latent Gaussian process prior
allows for the random partitions (i.e., clusters of the observations) to
exhibit dependencies among one another. The model is illustrated through the
analysis of a real data set arising from education, and through the analysis of
simulated data that were generated from complex data-generating models.
