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ORCIDAndrew Zammit-Mangion
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2020 – today
- 2024
[j12]Tin Lok James Ng, Andrew Zammit-Mangion:
Mixture modeling with normalizing flows for spherical density estimation. Adv. Data Anal. Classif. 18(1): 103-120 (2024)- [j11]Matthew Sainsbury-Dale, Andrew Zammit-Mangion, Noel Cressie
:
Modeling Big, Heterogeneous, Non-Gaussian Spatial and Spatio-Temporal Data Using FRK. J. Stat. Softw. 108(10) (2024) - [j10]Bao Anh Vu, David Gunawan, Andrew Zammit-Mangion:
R-VGAL: a sequential variational Bayes algorithm for generalised linear mixed models. Stat. Comput. 34(3): 110 (2024) - [j9]Bao Anh Vu, David Gunawan, Andrew Zammit-Mangion:
Correction to: R-VGAL: a sequential variational Bayes algorithm for generalised linear mixed models. Stat. Comput. 34(5): 169 (2024) - [i5]Andrew Zammit-Mangion, Matthew Sainsbury-Dale, Raphaël Huser:
Neural Methods for Amortised Parameter Inference. CoRR abs/2404.12484 (2024) - 2023
- [j8]Joshuah Jacobson, Noel Cressie, Andrew Zammit-Mangion:
Spatial Statistical Prediction of Solar-Induced Chlorophyll Fluorescence (SIF) from Multivariate OCO-2 Data. Remote. Sens. 15(16): 4038 (2023) - [i4]Andrew Zammit-Mangion, Michael D. Kaminski, Ba-Hien Tran, Maurizio Filippone, Noel Cressie:
Spatial Bayesian Neural Networks. CoRR abs/2311.09491 (2023) - 2022
- [i3]Christopher K. Wikle, Andrew Zammit-Mangion:
Statistical Deep Learning for Spatial and Spatio-Temporal Data. CoRR abs/2206.02218 (2022) - 2021
- [j7]Hsin-Cheng Huang, Noel Cressie, Andrew Zammit-Mangion, Guowen Huang:
False Discovery Rates to Detect Signals from Incomplete Spatially Aggregated Data. J. Comput. Graph. Stat. 30(4): 1081-1094 (2021) - [j6]Andrew Zammit-Mangion, Noel Cressie:
FRK: An R Package for Spatial and Spatio-Temporal Prediction with Large Datasets. J. Stat. Softw. 98(1) (2021) - [i2]Laura Cartwright, Andrew Zammit-Mangion, Nicholas M. Deutscher:
Emulation of greenhouse-gas sensitivities using variational autoencoders. CoRR abs/2112.12524 (2021) - 2020
- [j5]Andrew Zammit-Mangion, Jonathan Rougier:
Multi-scale process modelling and distributed computation for spatial data. Stat. Comput. 30(6): 1609-1627 (2020)
2010 – 2019
- 2019
- [i1]Andrew Zammit-Mangion, Christopher K. Wikle:
Deep Integro-Difference Equation Models for Spatio-Temporal Forecasting. CoRR abs/1910.13524 (2019) - 2018
- [j4]Andrew Zammit-Mangion, Jonathan Rougier:
A sparse linear algebra algorithm for fast computation of prediction variances with Gaussian Markov random fields. Comput. Stat. Data Anal. 123: 116-130 (2018) - [j3]Andrew Zammit-Mangion, Noel Cressie, Clint Shumack:
On Statistical Approaches to Generate Level 3 Products from Satellite Remote Sensing Retrievals. Remote. Sens. 10(1): 155 (2018) - 2013
- [b2]Andrew Zammit-Mangion, Michael Dewar, Visakan Kadirkamanathan, Anaid Flesken, Guido Sanguinetti:
Modeling Conflict Dynamics with Spatio-temporal Data. Springer Briefs in Applied Sciences and Technology, Springer 2013, ISBN 978-3-319-01037-3, pp. I-VIII, 1-74 - 2012
- [j2]Andrew Zammit-Mangion, Guido Sanguinetti, Visakan Kadirkamanathan:
Variational Estimation in Spatiotemporal Systems From Continuous and Point-Process Observations. IEEE Trans. Signal Process. 60(7): 3449-3459 (2012) - 2011
- [b1]Andrew Zammit-Mangion:
Modelling from spatiotemporal data : a dynamic systems approach. University of Sheffield, UK, 2011 - [j1]Andrew Zammit-Mangion, Ke Yuan, Visakan Kadirkamanathan, Mahesan Niranjan, Guido Sanguinetti:
Online Variational Inference for State-Space Models with Point-Process Observations. Neural Comput. 23(8): 1967-1999 (2011)
Coauthor Index
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