Online Variational Inference for State-Space Models with Point-Process Observations. - Andrew Zammit-Mangion; Ke Yuan; Visakan Kadirkamanathan; Mahesan Niranjan; Guido Sanguinetti (Neural Comput., 2011)

Modelling from spatiotemporal data : a dynamic systems approach. - Andrew Zammit-Mangion (2011)

Variational Estimation in Spatiotemporal Systems From Continuous and Point-Process Observations. - Andrew Zammit-Mangion; Guido Sanguinetti; Visakan Kadirkamanathan (IEEE Trans. Signal Process., 2012)

Modeling Conflict Dynamics with Spatio-temporal Data. - Andrew Zammit-Mangion; Michael Dewar; Visakan Kadirkamanathan; Anaid Flesken; Guido Sanguinetti (Springer, 2013)

A sparse linear algebra algorithm for fast computation of prediction variances with Gaussian Markov random fields. - Andrew Zammit-Mangion; Jonathan Rougier (Comput. Stat. Data Anal., 2018)

On Statistical Approaches to Generate Level 3 Products from Satellite Remote Sensing Retrievals. - Andrew Zammit-Mangion; Noel Cressie; Clint Shumack (Remote. Sens., 2018)

Deep Integro-Difference Equation Models for Spatio-Temporal Forecasting. - Andrew Zammit-Mangion; Christopher K. Wikle (CoRR, 2019)

Multi-scale process modelling and distributed computation for spatial data. - Andrew Zammit-Mangion; Jonathan Rougier (Stat. Comput., 2020)

Emulation of greenhouse-gas sensitivities using variational autoencoders. - Laura Cartwright; Andrew Zammit-Mangion; Nicholas M. Deutscher (CoRR, 2021)

False Discovery Rates to Detect Signals from Incomplete Spatially Aggregated Data. - Hsin-Cheng Huang; Noel Cressie; Andrew Zammit-Mangion; Guowen Huang (J. Comput. Graph. Stat., 2021)

FRK: An R Package for Spatial and Spatio-Temporal Prediction with Large Datasets. - Andrew Zammit-Mangion; Noel Cressie (J. Stat. Softw., 2021)

Statistical Deep Learning for Spatial and Spatio-Temporal Data. - Christopher K. Wikle; Andrew Zammit-Mangion (CoRR, 2022)

Spatial Bayesian Neural Networks. - Andrew Zammit-Mangion; Michael D. Kaminski; Ba-Hien Tran; Maurizio Filippone; Noel Cressie (CoRR, 2023)

WOMBAT v2.0 estimates of global GPP, respiration, and air-sea fluxes. - Michael Bertolacci; Andrew Zammit-Mangion; Andrew Schuh; Beata Bukosa; Jenny A. Fisher; Yi Cao; Aleya Kaushik; Noel Cressie (Zenodo, 2023)

mbertolacci/wombat-v2-workflow: WOMBAT: A fully Bayesian global flux-inversion framework, version 2.0. - Michael Bertolacci; Andrew Zammit-Mangion; Andrew Schuh; Beata Bukosa; Jenny A. Fisher; Yi Cao; Aleya Kaushik; Noel Cressie (Zenodo, 2023)

Spatial Statistical Prediction of Solar-Induced Chlorophyll Fluorescence (SIF) from Multivariate OCO-2 Data. - Joshuah Jacobson; Noel Cressie; Andrew Zammit-Mangion (Remote. Sens., 2023)

Neural Methods for Amortised Parameter Inference. - Andrew Zammit-Mangion; Matthew Sainsbury-Dale; Raphaël Huser (CoRR, 2024)

Modeling Big, Heterogeneous, Non-Gaussian Spatial and Spatio-Temporal Data Using FRK. - Matthew Sainsbury-Dale; Andrew Zammit-Mangion; Noel Cressie (J. Stat. Softw., 2024)

Mixture modeling with normalizing flows for spherical density estimation. - Tin Lok James Ng; Andrew Zammit-Mangion (Adv. Data Anal. Classif., 2024)

R-VGAL: a sequential variational Bayes algorithm for generalised linear mixed models. - Bao Anh Vu; David Gunawan; Andrew Zammit-Mangion (Stat. Comput., 2024)

Correction to: R-VGAL: a sequential variational Bayes algorithm for generalised linear mixed models. - Bao Anh Vu; David Gunawan; Andrew Zammit-Mangion (Stat. Comput., 2024)