Basis-function models in spatial statistics
N Cressie, M Sainsbury-Dale… - Annual Review of …, 2022 - annualreviews.org
Spatial statistics is concerned with the analysis of data that have spatial locations associated
with them, and those locations are used to model statistical dependence between the data …
with them, and those locations are used to model statistical dependence between the data …
A review of predictive uncertainty estimation with machine learning
H Tyralis, G Papacharalampous - Artificial Intelligence Review, 2024 - Springer
Predictions and forecasts of machine learning models should take the form of probability
distributions, aiming to increase the quantity of information communicated to end users …
distributions, aiming to increase the quantity of information communicated to end users …
Vecchia-approximated deep Gaussian processes for computer experiments
Abstract Deep Gaussian processes (DGPs) upgrade ordinary GPs through functional
composition, in which intermediate GP layers warp the original inputs, providing flexibility to …
composition, in which intermediate GP layers warp the original inputs, providing flexibility to …
Statistical deep learning for spatial and spatiotemporal data
CK Wikle, A Zammit-Mangion - Annual Review of Statistics and …, 2023 - annualreviews.org
Deep neural network models have become ubiquitous in recent years and have been
applied to nearly all areas of science, engineering, and industry. These models are …
applied to nearly all areas of science, engineering, and industry. These models are …
地理空间人工智能的近期研究总结与思考
高松 - 武汉大学学报(信息科学版), 2020 - ch.whu.edu.cn
人工智能领域的技术进步给地理空间相关领域研究的智能化发展和融合创新带来了新机遇和新
挑战. 地理空间人工智能(geospatial artificial intelligence, GeoAI) 是指地理空间科学与人工 …
挑战. 地理空间人工智能(geospatial artificial intelligence, GeoAI) 是指地理空间科学与人工 …
Deep integro-difference equation models for spatio-temporal forecasting
A Zammit-Mangion, CK Wikle - Spatial Statistics, 2020 - Elsevier
Integro-difference equation (IDE) models describe the conditional dependence between the
spatial process at a future time point and the process at the present time point through an …
spatial process at a future time point and the process at the present time point through an …
A review of probabilistic forecasting and prediction with machine learning
H Tyralis, G Papacharalampous - arXiv preprint arXiv:2209.08307, 2022 - arxiv.org
Predictions and forecasts of machine learning models should take the form of probability
distributions, aiming to increase the quantity of information communicated to end users …
distributions, aiming to increase the quantity of information communicated to end users …
A review of recent researches and reflections on geospatial artificial intelligence
S Gao - Geomatics and Information Science of Wuhan …, 2020 - ch.whu.edu.cn
The technological progress in the field of artificial intelligence (AI) has brought new
opportunities and challenges to the intelligent development and innovative research in …
opportunities and challenges to the intelligent development and innovative research in …
Thin and deep Gaussian processes
DA de Souza, A Nikitin, ST John… - Advances in …, 2024 - proceedings.neurips.cc
Gaussian processes (GPs) can provide a principled approach to uncertainty quantification
with easy-to-interpret kernel hyperparameters, such as the lengthscale, which controls the …
with easy-to-interpret kernel hyperparameters, such as the lengthscale, which controls the …
Spate-gan: Improved generative modeling of dynamic spatio-temporal patterns with an autoregressive embedding loss
From ecology to atmospheric sciences, many academic disciplines deal with data
characterized by intricate spatio-temporal complexities, the modeling of which often requires …
characterized by intricate spatio-temporal complexities, the modeling of which often requires …