10 citations · 40 across the 18 of their papers we have counts for
4 papers · 1 filter
Modern extreme value statistics for Utopian extremes
Jordan Richards, Noura Alotaibi, Daniela Cisneros +4
Capturing the extremal behaviour of data often requires bespoke marginal and dependence models which are grounded in rigorous asymptotic theory, and hence provide reliable extrapol…
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…
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…