50 citations · 248 across the 29 of their papers we have counts for
32 papers
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…
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…
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…
WOMBAT v2.S: A Bayesian inversion framework for attributing global CO flux components from multiprocess data
Josh Jacobson, Michael Bertolacci, Andrew Zammit-Mangion +2
Contributions from photosynthesis and other natural components of the carbon cycle present the largest uncertainties in our understanding of carbon dioxide (CO) sources and sin…
GeoWarp: Warped spatial processes for inferring subsea sediment properties
Michael Bertolacci, Andrew Zammit-Mangion, Juan Valderrama Giraldo +3
For offshore structures like wind turbines, subsea infrastructure, pipelines, and cables, it is crucial to quantify the properties of the seabed sediments at a proposed site. Howev…
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 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…
Spatial Bayesian Neural Networks
Andrew Zammit-Mangion, Michael D. Kaminski, Ba-Hien Tran +2
interpretable, and well understood models that are routinely employed even though, as is revealed through prior and posterior predictive checks, these can poorly characterise the s…
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…
Inferring changes to the global carbon cycle with WOMBAT v2.0, a hierarchical flux-inversion framework
Michael Bertolacci, Andrew Zammit-Mangion, Andrew Schuh +5
The natural cycles of the surface-to-atmosphere fluxes of carbon dioxide (CO) and other important greenhouse gases are changing in response to human influences. These changes n…
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…
Statistical Deep Learning for Spatial and Spatio-Temporal Data
Christopher K. Wikle, Andrew Zammit-Mangion
Deep neural network models have become ubiquitous in recent years, and have been applied to nearly all areas of science, engineering, and industry. These models are particularly us…
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…
Modelling Big, Heterogeneous, Non-Gaussian Spatial and Spatio-Temporal Data using FRK
Matthew Sainsbury-Dale, Andrew Zammit-Mangion, Noel Cressie
Non-Gaussian spatial and spatio-temporal data are becoming increasingly prevalent, and their analysis is needed in a variety of disciplines. FRK is an R package for spatial/spatio-…
From Many to One: Consensus Inference in a MIP
Noel Cressie, Michael Bertolacci, Andrew Zammit-Mangion
A Model Intercomparison Project (MIP) consists of teams who each estimate the same underlying quantity (e.g., temperature projections to the year 2070), and the spread of the estim…
Warped Gradient-Enhanced Gaussian Process Surrogate Models for Exponential Family Likelihoods with Intractable Normalizing Constants
Quan Vu, Matthew T. Moores, Andrew Zammit-Mangion
Markov chain Monte Carlo methods for exponential family models with intractable normalizing constant, such as the exchange algorithm, require simulations of the sufficient statisti…
WOMBAT: A fully Bayesian global flux-inversion framework
Andrew Zammit-Mangion, Michael Bertolacci, Jenny Fisher +4
WOMBAT (the WOllongong Methodology for Bayesian Assimilation of Trace-gases) is a fully Bayesian hierarchical statistical framework for flux inversion of trace gases from flask, in…
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 Integro-Difference Equation Models for Spatio-Temporal Forecasting
Andrew Zammit-Mangion, Christopher K. Wikle
Integro-difference equation (IDE) models describe the conditional dependence between the spatial process at a future time point and the process at the present time point through an…
Multi-Scale Process Modelling and Distributed Computation for Spatial Data
Andrew Zammit-Mangion, Jonathan Rougier
Recent years have seen a huge development in spatial modelling and prediction methodology, driven by the increased availability of remote-sensing data and the reduced cost of distr…