Andrew Zammit-Mangion

According to our database, Andrew Zammit-Mangion authored at least 21 papers between 2011 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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., October, 2024

R-VGAL: a sequential variational Bayes algorithm for generalised linear mixed models.

Bao Anh Vu, David Gunawan, Andrew Zammit-Mangion

Stat. Comput., June, 2024

Mixture modeling with normalizing flows for spherical density estimation.

Tin Lok James Ng, Andrew Zammit-Mangion

Adv. Data Anal. Classif., March, 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

Neural Methods for Amortised Parameter Inference.

Andrew Zammit-Mangion, Matthew Sainsbury-Dale, Raphaël Huser

CoRR, 2024

2023

Spatial Statistical Prediction of Solar-Induced Chlorophyll Fluorescence (SIF) from Multivariate OCO-2 Data.

Joshuah Jacobson, Noel Cressie, Andrew Zammit-Mangion

Remote. Sens., August, 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

Dataset, June, 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

Dataset, June, 2023

Spatial Bayesian Neural Networks.

Andrew Zammit-Mangion, Michael D. Kaminski, Ba-Hien Tran, Maurizio Filippone, Noel Cressie

CoRR, 2023

2022

Statistical Deep Learning for Spatial and Spatio-Temporal Data.

Christopher K. Wikle, Andrew Zammit-Mangion

CoRR, 2022

2021

FRK: An R Package for Spatial and Spatio-Temporal Prediction with Large Datasets.

Andrew Zammit-Mangion, Noel Cressie

J. Stat. Softw., 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

Emulation of greenhouse-gas sensitivities using variational autoencoders.

Laura Cartwright, Andrew Zammit-Mangion, Nicholas M. Deutscher

CoRR, 2021

2020

Multi-scale process modelling and distributed computation for spatial data.

Andrew Zammit-Mangion, Jonathan Rougier

Stat. Comput., 2020

2019

Deep Integro-Difference Equation Models for Spatio-Temporal Forecasting.

Andrew Zammit-Mangion, Christopher K. Wikle

CoRR, 2019

2018

On Statistical Approaches to Generate Level 3 Products from Satellite Remote Sensing Retrievals.

Andrew Zammit-Mangion, Noel Cressie, Clint Shumack

Remote. Sens., 2018

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

2013

Modeling Conflict Dynamics with Spatio-temporal Data.

Andrew Zammit-Mangion, Michael Dewar, Visakan Kadirkamanathan, Anaid Flesken, Guido Sanguinetti

Springer Briefs in Applied Sciences and Technology, Springer, ISBN: 978-3-319-01038-0, 2013

2012

Variational Estimation in Spatiotemporal Systems From Continuous and Point-Process Observations.

Andrew Zammit-Mangion, Guido Sanguinetti, Visakan Kadirkamanathan

IEEE Trans. Signal Process., 2012

2011

Modelling from spatiotemporal data : a dynamic systems approach.

Andrew Zammit-Mangion

PhD thesis, 2011

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