10 citations · 19 across the 14 of their papers we have counts for
5 papers · 1 filter
Neural Bayes Estimators for Irregular Spatial Data using Graph Neural Networks
Matthew Sainsbury-Dale, Andrew Zammit-Mangion, Jordan Richards +1
Neural Bayes estimators are neural networks that approximate Bayes estimators in a fast and likelihood-free manner. Although they are appealing to use with spatial models, where es…
Deep graphical regression for jointly moderate and extreme Australian wildfires
Daniela Cisneros, Jordan Richards, Ashok Dahal +2
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, d…
Extremal Dependence of Moving Average Processes Driven by Exponential-Tailed Lévy Noise
Zhongwei Zhang, David Bolin, Sebastian Engelke +1
Moving average processes driven by exponential-tailed Lévy noise are important extensions of their Gaussian counterparts in order to capture deviations from Gaussianity, more flexi…
Flexible and efficient emulation of spatial extremes processes via variational autoencoders
Likun Zhang, Xiaoyu Ma, Christopher K. Wikle +1
Many real-world processes have complex tail dependence structures that cannot be characterized using classical Gaussian processes. More flexible spatial extremes models exhibit app…
Neural Bayes estimators for censored inference with peaks-over-threshold models
Jordan Richards, Matthew Sainsbury-Dale, Andrew Zammit-Mangion +1
Making inference with spatial extremal dependence models can be computationally burdensome since they involve intractable and/or censored likelihoods. Building on recent advances i…