Fast covariance parameter estimation of spatial Gaussian process models using neural networks

30 December 2020

Papers citing "Fast covariance parameter estimation of spatial Gaussian process models using neural networks"

8 / 8 papers shown

Title
LatticeVision: Image to Image Networks for Modeling Non-Stationary Spatial Data Antony Sikorski Michael Ivanitskiy Nathan Lenssen Douglas Nychka Daniel McKenzie DiffM 29 0 0 14 May 2025
Modeling Spatial Extremes using Non-Gaussian Spatial Autoregressive Models via Convolutional Neural Networks Sweta Rai D. Nychka S. Bandyopadhyay 48 1 0 05 May 2025
Fast Likelihood-Free Parameter Estimation for Lévy Processes Nicolas Coloma William Kleiber 27 0 0 03 May 2025
Neural Likelihood Surfaces for Spatial Processes with Computationally Intensive or Intractable Likelihoods Julia Walchessen Amanda Lenzi Mikael Kuusela 43 9 0 31 Dec 2024
When the whole is greater than the sum of its parts: Scaling black-box inference to large data settings through divide-and-conquer Emily C. Hector Amanda Lenzi 48 1 0 31 Dec 2024
Neural Bayes Estimators for Irregular Spatial Data using Graph Neural Networks Matthew Sainsbury-Dale A. Zammit‐Mangion J. Richards Raphael Huser 36 15 0 04 Oct 2023
Spherical Poisson Point Process Intensity Function Modeling and Estimation with Measure Transport T. L. J. Ng A. Zammit‐Mangion 39 3 0 24 Jan 2022
Neural Networks for Parameter Estimation in Intractable Models Amanda Lenzi J. Bessac J. Rudi Michael L. Stein BDL 31 50 0 29 Jul 2021