9 citations · 34 across the 6 of their papers we have counts for
4 papers
Fast Bayesian inference of Block Nearest Neighbor Gaussian process for large data
Zaida C. Quiroz, Marcos O. Prates, Dipak K. Dey +1
This paper presents the development of a spatial block-Nearest Neighbor Gaussian process (block-NNGP) for location-referenced large spatial data. The key idea behind this approach…
Multivariate Gaussian Random Fields with Oscillating Covariance Functions using Systems of Stochastic Partial Differential Equations
Xiangping Hu, Finn Lindgren, Daniel Simpson +1
In this paper we propose a new approach for constructing \emph{multivariate} Gaussian random fields (GRFs) with oscillating covariance functions through systems of stochastic parti…
Spatial Modelling of Temperature and Humidity using Systems of Stochastic Partial Differential Equations
Xiangping Hu, Ingelin Steinsland, Daniel Simpson +2
This work is motivated by constructing a weather simulator for precipitation. Temperature and humidity are two of the most important driving forces of precipitation, and the strate…
Bayesian Adaptive Smoothing Spline using Stochastic Differential Equations
Yu Ryan Yue, Daniel Simpson, Finn Lindgren +1
The smoothing spline is one of the most popular curve-fitting methods, partly because of empirical evidence supporting its effectiveness and partly because of its elegant mathemati…