10 papers
Separation-based causal discovery for extremes
Junshu Jiang, Jordan Richards, Raphaël Huser +1
Structural causal models (SCMs), with an underlying directed acyclic graph (DAG), provide a powerful analytical framework to describe the interaction mechanisms in large-scale comp…
Modeling Nonstationary Extremal Dependence via Deep Spatial Deformations
Xuanjie Shao, Jordan Richards, Raphael Huser
Modeling nonstationarity that often prevails in extremal dependence of spatial data can be challenging, and typically requires bespoke or complex spatial models that are difficult…
Neural Parameter Estimation with Incomplete Data
Matthew Sainsbury-Dale, Andrew Zammit-Mangion, Noel Cressie +1
Advances in artificial intelligence (AI) and deep learning have led to neural networks being used to generate lightning-speed answers to complex science questions, paintings in the…
deepspat: An R package for modeling nonstationary spatial and spatio-temporal Gaussian and extremes data through deep deformations
Quan Vu, Xuanjie Shao, Raphaël Huser +1
Nonstationarity in spatial and spatio-temporal processes is ubiquitous in environmental datasets, but is not often addressed in practice, due to a scarcity of statistical software…
Neural Conditional Simulation for Complex Spatial Processes
Julia Walchessen, Andrew Zammit-Mangion, Raphaël Huser +1
A key objective in spatial statistics is to simulate from the distribution of a spatial process at a selection of unobserved locations conditional on observations (i.e., a predicti…
Generative modelling of multivariate geometric extremes using normalising flows
Lambert De Monte, Raphaël Huser, Ioannis Papastathopoulos +1
Leveraging the recently emerging geometric approach to multivariate extremes and the flexibility of normalising flows on the hypersphere, we propose a principled deep-learning-base…