Radial neighbours for provably accurate scalable approximations of Gaussian processes
In geostatistical problems with massive sample size, Gaussian processes can be
approximated using sparse directed acyclic graphs to achieve scalable O (n) computational …
approximated using sparse directed acyclic graphs to achieve scalable O (n) computational …
Inside-out cross-covariance for spatial multivariate data
M Peruzzi - arXiv preprint arXiv:2412.12407, 2024 - arxiv.org
As the spatial features of multivariate data are increasingly central in researchers' applied
problems, there is a growing demand for novel spatially-aware methods that are flexible …
problems, there is a growing demand for novel spatially-aware methods that are flexible …
Spatial predictions on physically constrained domains: Applications to Arctic sea salinity data
(i) Additional algorithm and explanatory text for BORA-GP neighbor selection;(ii) prior
specifications and more results for model comparisons;(iii) posterior convergence …
specifications and more results for model comparisons;(iii) posterior convergence …
Nearest-neighbor mixture models for non-Gaussian spatial processes
X Zheng, A Kottas, B Sansó - Bayesian Analysis, 2023 - projecteuclid.org
Nearest-Neighbor Mixture Models for Non-Gaussian Spatial Processes Page 1 Bayesian
Analysis (2023) 18, Number 4, pp. 1191–1222 Nearest-Neighbor Mixture Models for Non-Gaussian …
Analysis (2023) 18, Number 4, pp. 1191–1222 Nearest-Neighbor Mixture Models for Non-Gaussian …
Methods of Model Uncertainty: Bayesian Spatial Predictive Synthesis
DE Cabel - 2024 - search.proquest.com
This dissertation develops a new method of modeling uncertainty with spatial data called
Bayesian spatial predictive synthesis (BSPS) and compares its predictive accuracy to …
Bayesian spatial predictive synthesis (BSPS) and compares its predictive accuracy to …
Some Advances in Nonparametric Statistics
Y Zhu - 2023 - search.proquest.com
Nonparametric statistics is an important branch of statistics that utilizes infinite dimensional
models to achieve great flexibility. However, such flexibility often comes with difficulties in …
models to achieve great flexibility. However, such flexibility often comes with difficulties in …