By Associate Professor Andrew Zammit Mangion

Journal articles

Zammit-Mangion A; Wikle CK, 2020, 'Deep integro-difference equation models for spatio-temporal forecasting', Spatial Statistics, 37, http://dx.doi.org/10.1016/j.spasta.2020.100408

Yoo EH; Zammit-Mangion A; Chipeta MG, 2020, 'Adaptive spatial sampling design for environmental field prediction using low-cost sensing technologies', Atmospheric Environment, 221, http://dx.doi.org/10.1016/j.atmosenv.2019.117091

Heaton MJ; Datta A; Finley AO; Furrer R; Guinness J; Guhaniyogi R; Gerber F; Gramacy RB; Hammerling D; Katzfuss M; Lindgren F; Nychka DW; Sun F; Zammit-Mangion A, 2019, 'A Case Study Competition Among Methods for Analyzing Large Spatial Data', Journal of Agricultural Biological and Environmental Statistics, 24, pp. 398 - 425, http://dx.doi.org/10.1007/s13253-018-00348-w

Cartwright L; Zammit-Mangion A; Bhatia S; Schroder I; Phillips F; Coates T; Negandhi K; Naylor T; Kennedy M; Zegelin S; Wokker N; Deutscher NM; Feitz A, 2019, 'Bayesian atmospheric tomography for detection and quantification of methane emissions: Application to data from the 2015 Ginninderra release experiment', Atmospheric Measurement Techniques, 12, pp. 4659 - 4676, http://dx.doi.org/10.5194/amt-12-4659-2019

Suesse T; Zammit-Mangion A, 2019, 'Marginal maximum likelihood estimation of conditional autoregressive models with missing data', Stat, 8, http://dx.doi.org/10.1002/sta4.226

Zammit-Mangion A; Rougier J, 2018, 'A sparse linear algebra algorithm for fast computation of prediction variances with Gaussian Markov random fields', Computational Statistics and Data Analysis, 123, pp. 116 - 130, http://dx.doi.org/10.1016/j.csda.2018.02.001

Zammit-Mangion A; Cressie N; Shumack C, 2018, 'On statistical approaches to generate Level 3 products from satellite remote sensing retrievals', Remote Sensing, 10, http://dx.doi.org/10.3390/rs10010155

Suesse T; Zammit-Mangion A, 2017, 'Computational aspects of the EM algorithm for spatial econometric models with missing data', Journal of Statistical Computation and Simulation, 87, pp. 1767 - 1786, http://dx.doi.org/10.1080/00949655.2017.1286495

Martin-Español A; Bamber JL; Zammit-Mangion A, 2017, 'Constraining the mass balance of East Antarctica', Geophysical Research Letters, 44, pp. 4168 - 4175, http://dx.doi.org/10.1002/2017GL072937

Cressie N; Zammit-Mangion A, 2016, 'Multivariate spatial covariance models: A conditional approach', Biometrika, 103, pp. 915 - 935, http://dx.doi.org/10.1093/biomet/asw045

Rougier J; Zammit-Mangion A, 2016, 'Visualization for Large-scale Gaussian Updates', Scandinavian Journal of Statistics, 43, pp. 1153 - 1161, http://dx.doi.org/10.1111/sjos.12234

Zammit-Mangion A; Cressie N; Ganesan AL, 2016, 'Non-Gaussian bivariate modelling with application to atmospheric trace-gas inversion', Spatial Statistics, 18, pp. 194 - 220, http://dx.doi.org/10.1016/j.spasta.2016.06.005

Cseke B; Zammit-Mangion A; Heskes T; Sanguinetti G, 2016, 'Sparse Approximate Inference for Spatio-Temporal Point Process Models', Journal of the American Statistical Association, 111, pp. 1746 - 1763, http://dx.doi.org/10.1080/01621459.2015.1115357

Martín-Español A; King MA; Zammit-Mangion A; Andrews SB; Moore P; Bamber JL, 2016, 'An assessment of forward and inverse GIA solutions for Antarctica', Journal of Geophysical Research Solid Earth, 121, pp. 6947 - 6965, http://dx.doi.org/10.1002/2016JB013154

Martín-Español A; Zammit-Mangion A; Clarke PJ; Flament T; Helm V; King MA; Luthcke SB; Petrie E; Rémy F; Schön N; Wouters B; Bamber JL, 2016, 'Spatial and temporal Antarctic Ice Sheet mass trends, glacio-isostatic adjustment, and surface processes from a joint inversion of satellite altimeter, gravity, and GPS data', Journal of Geophysical Research Earth Surface, 121, pp. 182 - 200, http://dx.doi.org/10.1002/2015JF003550

Zammit-Mangion A; Cressie N; Ganesan AL; O'Doherty S; Manning AJ, 2015, 'Spatio-temporal bivariate statistical models for atmospheric trace-gas inversion', Chemometrics and Intelligent Laboratory Systems, 149, pp. 227 - 241, http://dx.doi.org/10.1016/j.chemolab.2015.09.006

Zammit-Mangion A; Rougier J; Schön N; Lindgren F; Bamber J, 2015, 'Multivariate spatio-temporal modelling for assessing Antarctica's present-day contribution to sea-level rise', Environmetrics, 26, pp. 159 - 177, http://dx.doi.org/10.1002/env.2323

Schoen N; Zammit-Mangion A; Rougier JC; Flament T; Rémy F; Luthcke S; Bamber JL, 2015, 'Simultaneous solution for mass trends on the West Antarctic Ice Sheet', Cryosphere, 9, pp. 805 - 819, http://dx.doi.org/10.5194/tc-9-805-2015

Zammit-Mangion A; Bamber JL; Schoen NW; Rougier JC, 2015, 'A data-driven approach for assessing ice-sheet mass balance in space and time', Annals of Glaciology, 56, pp. 175 - 183, http://dx.doi.org/10.3189/2015AoG70A021

Cressie N; Burden S; Davis W; Krivitsky PN; Mokhtarian P; Suesse T; Zammit-Mangion A, 2015, 'Capturing multivariate spatial dependence: Model, estimate and then predict', Statistical Science, 30, pp. 170 - 175, http://dx.doi.org/10.1214/15-STS517

Ganesan AL; Rigby M; Zammit-Mangion A; Manning AJ; Prinn RG; Fraser PJ; Harth CM; Kim KR; Krummel PB; Li S; Mühle J; O'Doherty SJ; Park S; Salameh PK; Steele LP; Weiss RF, 2014, 'Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods', Atmospheric Chemistry and Physics, 14, pp. 3855 - 3864, http://dx.doi.org/10.5194/acp-14-3855-2014

Zammit-Mangion A; Rougier J; Bamber J; Schön N, 2014, 'Resolving the Antarctic contribution to sea-level rise: A hierarchical modelling framework', Environmetrics, 25, pp. 245 - 264, http://dx.doi.org/10.1002/env.2247

Menzies RI; Zammit-Mangion A; Hollis LM; Lennen RJ; Jansen MA; Webb DJ; Mullins JJ; Dear JW; Sanguinetti G; Bailey MA, 2013, 'An anatomically unbiased approach for analysis of renal BOLD magnetic resonance images', American Journal of Physiology Renal Physiology, 305, pp. F845 - F852, http://dx.doi.org/10.1152/ajprenal.00113.2013

Zammit-Mangion A; Dewar M; Kadirkamanathan V; Flesken A; Sanguinetti G, 2013, 'Conflict data sets and point patterns', Springerbriefs in Applied Sciences and Technology, pp. 1 - 14, http://dx.doi.org/10.1007/978-3-319-01038-0_1

Zammit-Mangion A, 2013, 'Foreword', Springerbriefs in Applied Sciences and Technology, pp. i - iv

Zammit-Mangion A; Dewar M; Kadirkamanathan V; Flesken A; Sanguinetti G, 2013, 'Modeling and prediction in conflict: Afghanistan', Springerbriefs in Applied Sciences and Technology, pp. 47 - 66, http://dx.doi.org/10.1007/978-3-319-01038-0_3

Zammit-Mangion A; Dewar M; Kadirkamanathan V; Flesken A; Sanguinetti G, 2013, 'Theory', Springerbriefs in Applied Sciences and Technology, pp. 15 - 46, http://dx.doi.org/10.1007/978-3-319-01038-0_2

Zammit-Mangion A; Dewar M; Kadirkamanathan V; Sanguinetti G, 2012, 'Point process modelling of the Afghan War Diary', Proceedings of the National Academy of Sciences of the United States of America, 109, pp. 12414 - 12419, http://dx.doi.org/10.1073/pnas.1203177109

Zammit-Mangion A; Sanguinetti G; Kadirkamanathan V, 2012, 'Variational estimation in spatiotemporal systems from continuous and point-process observations', IEEE Transactions on Signal Processing, 60, pp. 3449 - 3459, http://dx.doi.org/10.1109/TSP.2012.2191966

Zammit Mangion A; Yuan K; Kadirkamanathan V; Niranjan M; Sanguinetti G, 2011, 'Online variational inference for state-space models with point-process observations', Neural Computation, 23, pp. 1967 - 1999, http://dx.doi.org/10.1162/NECO_a_00156

Zammit Mangion A; Sanguinetti G; Kadirkamanathan V, 2011, 'A variational approach for the online dual estimation of spatiotemporal systems governed by the IDE', IFAC Proceedings Volumes IFAC Papersonline, 44, pp. 3204 - 3209, http://dx.doi.org/10.3182/20110828-6-IT-1002.02459

Zammit Mangion A; Anderson SR; Kadirkamanathan V, 2011, 'Exploration and control of stochastic spatiotemporal systems with mobile agents', IFAC Proceedings Volumes IFAC Papersonline, 44, pp. 4489 - 4494, http://dx.doi.org/10.3182/20110828-6-IT-1002.01503

Conference Papers

Mills AR; Apopei B; Mangion AZ; Barron-Gonzales H; Gunetti P; Thompson HA; Garbett P, 2010, 'Heterogeneous hardware technologies for accelerating complex aerospace system simulations', in IEEE Aerospace Conference Proceedings, http://dx.doi.org/10.1109/AERO.2010.5446789

Knake-Langhorst S; Zammit-Mangion A, 2008, 'Usability of local traffic density as basis for advanced driver assistance systems', in Fisita World Automotive Congress 2008 Congress Proceedings Vehicle Safety, pp. 344 - 353

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

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