A0346
Title: Fast Bayesian inference of block nearest neighbor Gaussian models for large data
Authors: Zaida Quiroz - Pontificia Universidad Catolica del Peru (Peru) [presenting]
Marcos Prates - Universidade Federal de Minas Gerais (Brazil)
Dipak Dey - UCONN (United States)
Haavard Rue - KAUST (Saudi Arabia)
Abstract: The purpose is to present the development of a spatial block-nearest neighbor Gaussian process (blockNNGP) for location-referenced large spatial data. The key idea behind this approach is to divide the spatial domain into several blocks, which are dependent on some constraints. The cross-blocks capture the large-scale spatial dependence, while each block captures the small-scale spatial dependence. The resulting blockNNGP enjoys Markov properties reflected on its sparse precision matrix. It is embedded as a prior within the class of latent Gaussian models. Thus, fast Bayesian inference is obtained using the integrated nested Laplace approximation. The performance of the blockNNGP is illustrated on simulated examples, a comparison of our approach with other methods for analyzing large spatial data and applications with Gaussian and non-Gaussian real data.
