50 citations · 248 across the 29 of their papers we have counts for
15 papers · 1 filter
Spatio-temporal modeling and forecasting with Fourier neural operators
Pratik Nag, Andrew Zammit-Mangion, Sumeetpal Singh +1
Spatio-temporal process models are often used for modeling dynamic physical and biological phenomena that evolve across space and time. These phenomena may exhibit environmental he…
Modeling nonstationary spatial processes with normalizing flows
Pratik Nag, Andrew Zammit-Mangion, Ying Sun
Nonstationary spatial processes can often be represented as stationary processes on a warped spatial domain. Selecting an appropriate spatial warping function for a given applicati…
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…
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…
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…
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…
Adaptive Spatial Sampling Design for Environmental Field Prediction using Low-Cost Sensing Technologies
Eun-Hye Yoo, Andrew Zammit-Mangion, Michael G. Chipeta
The last decade has seen an explosion in data sources available for the monitoring and prediction of environmental phenomena. While several inferential methods have been developed…
Likelihood-Free Parameter Estimation with Neural Bayes Estimators
Matthew Sainsbury-Dale, Andrew Zammit-Mangion, Raphaël Huser
Neural point estimators are neural networks that map data to parameter point estimates. They are fast, likelihood free and, due to their amortised nature, amenable to fast bootstra…
Basis-Function Models in Spatial Statistics
Noel Cressie, Matthew Sainsbury-Dale, Andrew Zammit-Mangion
Spatial statistics is concerned with the analysis of data that have spatial locations associated with them, and those locations are used to model statistical dependence between the…
Constructing Large Nonstationary Spatio-Temporal Covariance Models via Compositional Warpings
Quan Vu, Andrew Zammit-Mangion, Stephen J. Chuter
Understanding and predicting environmental phenomena often requires the construction of spatio-temporal statistical models, which are typically Gaussian processes. A common assumpt…
Modeling Nonstationary and Asymmetric Multivariate Spatial Covariances via Deformations
Quan Vu, Andrew Zammit-Mangion, Noel Cressie
Multivariate spatial-statistical models are often used when modeling environmental and socio-demographic processes. The most commonly used models for multivariate spatial covarianc…
Deep Compositional Spatial Models
Andrew Zammit-Mangion, Tin Lok James Ng, Quan Vu +1
Spatial processes with nonstationary and anisotropic covariance structure are often used when modelling, analysing and predicting complex environmental phenomena. Such processes ma…
False Discovery Rates to Detect Signals from Incomplete Spatially Aggregated Data
Hsin-Cheng Huang, Noel Cressie, Andrew Zammit-Mangion +1
There are a number of ways to test for the absence/presence of a spatial signal in a completely observed fine-resolution image. One of these is a powerful nonparametric procedure c…
Capturing Multivariate Spatial Dependence: Model, Estimate and then Predict
Noel Cressie, Sandy Burden, Walter Davis +4
Physical processes rarely occur in isolation, rather they influence and interact with one another. Thus, there is great benefit in modeling potential dependence between both spatia…
Multivariate Spatial Covariance Models: A Conditional Approach
Noel Cressie, Andrew Zammit-Mangion
Multivariate geostatistics is based on modelling all covariances between all possible combinations of two or more variables at any sets of locations in a continuously indexed domai…