By Associate Professor Andrew Zammit Mangion

Preprints

Walchessen J; Zammit-Mangion A; Huser R; Kuusela M, 2026, Neural Conditional Simulation for Complex Spatial Processes, http://dx.doi.org/10.48550/arxiv.2508.20067

Ng TLJ; Kwong K-K; Liu J; Zammit-Mangion A, 2026, Bayesian Sphere-on-Sphere Regression with Optimal Transport Maps, http://dx.doi.org/10.48550/arxiv.2501.08492

Sainsbury-Dale M; Zammit-Mangion A; Cressie N; Huser R, 2026, Neural Parameter Estimation with Incomplete Data, http://dx.doi.org/10.48550/arxiv.2501.04330

Nag P; Zammit-Mangion A; Singh S; Cressie N, 2026, Spatio-temporal modeling and forecasting with Fourier neural operators, http://dx.doi.org/10.48550/arxiv.2601.01813

Patterson C; Zammit-Mangion A; Xue Z; Zhang K; Zhou Z; Timms W; Feitz A, 2026, The Otway Shallow Fault Experiment: Insights from surface monitoring, http://dx.doi.org/10.2139/ssrn.6041639

Vu Q; Shao X; Huser R; Zammit-Mangion A, 2025, deepspat: An R package for modeling nonstationary spatial and spatio-temporal Gaussian and extremes data through deep deformations, http://dx.doi.org/10.48550/arxiv.2512.08137

Jacobson J; Bertolacci M; Zammit-Mangion A; Schuh A; Cressie N, 2025, WOMBAT v2.S: A Bayesian inversion framework for attributing global CO$_2$ flux components from multiprocess data, http://dx.doi.org/10.48550/arxiv.2503.09065

Bertolacci M; Zammit-Mangion A; Giraldo JV; O'Neill M; Bransby F; Watson P, 2025, GeoWarp: Warped spatial processes for inferring subsea sediment properties, http://dx.doi.org/10.48550/arxiv.2501.07841

Sainsbury-Dale M; Zammit-Mangion A; Richards J; Huser R, 2025, Neural Bayes Estimators for Irregular Spatial Data using Graph Neural Networks, http://dx.doi.org/10.48550/arxiv.2310.02600

Zammit-Mangion A; Sainsbury-Dale M; Huser R, 2024, Neural Methods for Amortized Inference, http://dx.doi.org/10.48550/arxiv.2404.12484

Zammit-Mangion A; Kaminski MD; Tran B-H; Filippone M; Cressie N, 2024, Spatial Bayesian Neural Networks, http://dx.doi.org/10.48550/arxiv.2311.09491

Sainsbury-Dale M; Zammit-Mangion A; Huser R, 2023, Likelihood-Free Parameter Estimation with Neural Bayes Estimators, http://dx.doi.org/10.48550/arxiv.2208.12942

Vu Q; Zammit-Mangion A; Chuter SJ, 2023, Constructing Large Nonstationary Spatio-Temporal Covariance Models via Compositional Warpings, http://dx.doi.org/10.48550/arxiv.2202.03560

Yoo E-H; Zammit-Mangion A; Chipeta MG, 2023, Adaptive Spatial Sampling Design for Environmental Field Prediction using Low-Cost Sensing Technologies, http://dx.doi.org/10.48550/arxiv.2303.02050

Vu Q; Moores MT; Zammit-Mangion A, 2023, Warped Gradient-Enhanced Gaussian Process Surrogate Models for Exponential Family Likelihoods with Intractable Normalizing Constants, http://dx.doi.org/10.48550/arxiv.2105.04374

Ng TLJ; Zammit-Mangion A, 2023, Mixture Modeling with Normalizing Flows for Spherical Density Estimation, http://dx.doi.org/10.48550/arxiv.2301.06404

Sainsbury-Dale M; Zammit-Mangion A; Cressie N, 2022, Modelling Big, Heterogeneous, Non-Gaussian Spatial and Spatio-Temporal Data using FRK, http://dx.doi.org/10.48550/arxiv.2110.02507

Bertolacci M; Zammit-Mangion A; Schuh A; Bukosa B; Fisher J; Cao Y; Kaushik A; Cressie N, 2022, Inferring changes to the global carbon cycle with WOMBAT v2.0, a hierarchical flux-inversion framework, http://dx.doi.org/10.48550/arxiv.2210.10479

Wikle CK; Zammit-Mangion A, 2022, Statistical Deep Learning for Spatial and Spatio-Temporal Data, http://dx.doi.org/10.48550/arxiv.2206.02218

Ng TLJ; Zammit-Mangion A, 2022, Non-Homogeneous Poisson Process Intensity Modeling and Estimation using Measure Transport, http://dx.doi.org/10.48550/arxiv.2007.00248

Cressie N; Sainsbury-Dale M; Zammit-Mangion A, 2022, Basis-Function Models in Spatial Statistics, http://dx.doi.org/10.48550/arxiv.2202.03660

Ng TLJ; Zammit-Mangion A, 2022, Spherical Poisson Point Process Intensity Function Modeling and Estimation with Measure Transport, http://dx.doi.org/10.48550/arxiv.2201.09485

Huang H-C; Cressie N; Zammit-Mangion A; Huang G, 2020, False Discovery Rates to Detect Signals from Incomplete Spatially Aggregated Data, http://dx.doi.org/10.48550/arxiv.1905.06268

Zammit-Mangion A; Ng TLJ; Vu Q; Filippone M, 2020, Deep Compositional Spatial Models, http://dx.doi.org/10.48550/arxiv.1906.02840

Zammit-Mangion A; Rougier J, 2020, Multi-Scale Process Modelling and Distributed Computation for Spatial Data, http://dx.doi.org/10.48550/arxiv.1907.07813

Zammit-Mangion A; Wikle CK, 2020, Deep Integro-Difference Equation Models for Spatio-Temporal Forecasting, http://dx.doi.org/10.48550/arxiv.1910.13524

Zammit-Mangion A; Cressie N, 2018, FRK: An R Package for Spatial and Spatio-Temporal Prediction with Large Datasets, http://dx.doi.org/10.48550/arxiv.1705.08105

Heaton MJ; Datta A; Finley A; Furrer R; Guhaniyogi R; Gerber F; Gramacy RB; Hammerling D; Katzfuss M; Lindgren F; Nychka DW; Sun F; Zammit-Mangion A, 2018, A Case Study Competition Among Methods for Analyzing Large Spatial Data, http://dx.doi.org/10.48550/arxiv.1710.05013

Zammit-Mangion A; Rougier J, 2018, A sparse linear algebra algorithm for fast computation of prediction variances with Gaussian Markov random fields, http://dx.doi.org/10.48550/arxiv.1707.00892

Zammit-Mangion A; Cressie N; Shumack C, 2018, On statistical approaches to generate Level 3 products from satellite remote sensing retrievals, http://dx.doi.org/10.48550/arxiv.1711.07629

Cressie N; Zammit-Mangion A, 2016, Multivariate Spatial Covariance Models: A Conditional Approach, http://dx.doi.org/10.48550/arxiv.1504.01865

Zammit-Mangion A; Cressie N; Ganesan AL, 2016, Non-Gaussian bivariate modelling with application to atmospheric trace-gas inversion, http://dx.doi.org/10.48550/arxiv.1606.04564

Zammit-Mangion A; Cressie N; Ganesan AL; Doherty SO; Manning AJ, 2015, Spatio-temporal bivariate statistical models for atmospheric trace-gas inversion, http://dx.doi.org/10.48550/arxiv.1509.00915

Cartwright L; Zammit-Mangion A; Bhatia S; Schroder I; Phillips F; Coates T; Neghandhi K; Naylor T; Kennedy M; Zegelin S; Wokker N; Deutscher NM; Feitz A, Bayesian atmospheric tomography for detection and quantification of methane emissions: Application to data from the 2015 Ginninderra release experiment, http://dx.doi.org/10.5194/amt-2019-124

Chuter SJ; Zammit-Mangion A; Rougier J; Dawson G; Bamber JL, Mass evolution of the Antarctic Peninsula over the last two decades from a joint Bayesian inversion, http://dx.doi.org/10.5194/tc-2021-178

Stell AC; Bertolacci M; Zammit-Mangion A; Rigby M; Fraser PJ; Harth CM; Krummel PB; Lan X; Manizza M; Mühle J; O'Doherty S; Prinn RG; Weiss RF; Young D; Ganesan AL, Modelling the growth of atmospheric nitrous oxide using a global hierarchical inversion, http://dx.doi.org/10.5194/egusphere-2022-513

Beck B; Zammit-Mangion A; Fry R; Smith K; Gabbe B, Spatiotemporal mapping of major trauma in Victoria, Australia, http://dx.doi.org/10.1101/2021.11.21.21266663

Zammit-Mangion A; Bertolacci M; Fisher J; Stavert A; Rigby ML; Cao Y; Cressie N, WOMBAT v1.0: A fully Bayesian global flux-inversion framework, http://dx.doi.org/10.5194/gmd-2021-181

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