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Found 6 papers, 5 papers with code
Neural Bayes Estimators for Irregular Spatial Data using Graph Neural Networks
Neural Bayes estimators are neural networks that approximate Bayes estimators in a fast and likelihood-free manner.
Deep graphical regression for jointly moderate and extreme Australian wildfires
Recent wildfires in Australia have led to considerable economic loss and property destruction, and there is increasing concern that climate change may exacerbate their intensity, duration, and frequency.
Neural Bayes estimators for censored inference with peaks-over-threshold models
Making inference with spatial extremal dependence models can be computationally burdensome since they involve intractable and/or censored likelihoods.
Insights into the drivers and spatio-temporal trends of extreme Mediterranean wildfires with statistical deep-learning
Our results highlight that whilst VPD, air temperature, and drought significantly affect wildfire occurrence, only VPD affects wildfire spread.
Regression modelling of spatiotemporal extreme U.S. wildfires via partially-interpretable neural networks
In this paper, we propose a new methodological framework for performing extreme quantile regression using artificial neutral networks, which are able to capture complex non-linear relationships and scale well to high-dimensional data.
Spatial deformation for non-stationary extremal dependence
Modelling the extremal dependence structure of spatial data is considerably easier if that structure is stationary.
Methodology Applications
