4 papers · 1 filter
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 Methods for Amortized Inference
Andrew Zammit-Mangion, Matthew Sainsbury-Dale, Raphaël Huser
Simulation-based methods for statistical inference have evolved dramatically over the past 50 years, keeping pace with technological advancements. The field is undergoing a new rev…
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…
Extreme quantile regression with deep learning
Jordan Richards, Raphaël Huser
Estimation of extreme conditional quantiles is often required for risk assessment of natural hazards in climate and geo-environmental sciences and for quantitative risk management…