A Note on the comparison of Nearest Neighbor Gaussian Process (NNGP) based models
Statistics Theory
2018-11-12 v1 Statistics Theory
Abstract
This note is devoted to the comparison between two Nearest-neighbor Gaussian processes (NNGP) based models: the response NNGP model and the latent NNGP model. We exhibit that the comparison based on the Kullback-Leibler divergence (KL-D) from the NNGP based models to their parent GP based model can result in reverse conclusions in different parameter spaces. And we suggest a heuristic explanation on the phenomenon that the latent NNGP model tends to outperform the response NNGP model in approximating their parent GP based model.
Keywords
Cite
@article{arxiv.1811.03735,
title = {A Note on the comparison of Nearest Neighbor Gaussian Process (NNGP) based models},
author = {Lu Zhang and Sudipto Banerjee},
journal= {arXiv preprint arXiv:1811.03735},
year = {2018}
}