Andrew Zammit Mangion | DeepAI

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Featured Co-authors

Raphaël Huser
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Maurizio Filippone
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Tom Heskes
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Christopher K. Wikle
25 publications
Guido Sanguinetti
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David Gunawan
17 publications
Noel Cressie
13 publications
Yi Cao
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Tin Lok James Ng
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Botond Cseke
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Matthew T. Moores
6 publications

∙ 06/01/2023

R-VGAL: A Sequential Variational Bayes Algorithm for Generalised Linear Mixed Models

Models with random effects, such as generalised linear mixed models (GLM...

0 Bao Anh Vu, et al. ∙

∙ 03/03/2023

Adaptive Spatial Sampling Design for Environmental Field Prediction using Low-Cost Sensing Technologies

The last decade has seen an explosion in data sources available for the ...

0 Eun-Hye Yoo, et al. ∙

∙ 01/16/2023

Mixture Modeling with Normalizing Flows for Spherical Density Estimation

Normalizing flows are objects used for modeling complicated probability ...

0 Tin Lok James Ng, et al. ∙

∙ 10/19/2022

Inferring changes to the global carbon cycle with WOMBAT v2.0, a hierarchical flux-inversion framework

The natural cycles of the surface-to-atmosphere fluxes of carbon dioxide...

0 Michael Bertolacci, et al. ∙

∙ 08/27/2022

Fast Optimal Estimation with Intractable Models using Permutation-Invariant Neural Networks

Neural networks have recently shown promise for likelihood-free inferenc...

22 Matthew Sainsbury-Dale, et al. ∙

∙ 06/05/2022

Statistical Deep Learning for Spatial and Spatio-Temporal Data

Deep neural network models have become ubiquitous in recent years, and h...

0 Christopher K. Wikle, et al. ∙

∙ 02/08/2022

Basis-Function Models in Spatial Statistics

Spatial statistics is concerned with the analysis of data that have spat...

0 Noel Cressie, et al. ∙

∙ 02/07/2022

Constructing Large Nonstationary Spatio-Temporal Covariance Models via Compositional Warpings

Understanding and predicting environmental phenomena often requires the ...

0 Quan Vu, et al. ∙

∙ 01/24/2022

Spherical Poisson Point Process Intensity Function Modeling and Estimation with Measure Transport

Recent years have seen an increased interest in the application of metho...

0 Tin Lok James Ng, et al. ∙

∙ 12/22/2021

Emulation of greenhouse-gas sensitivities using variational autoencoders

Flux inversion is the process by which sources and sinks of a gas are id...

0 Laura Cartwright, et al. ∙

∙ 12/20/2021

A Review of Bayesian Modelling in Glaciology

Bayesian methods for modelling and inference are being increasingly used...

0 Giri Gopalan, et al. ∙

∙ 10/06/2021

Modelling, Fitting, and Prediction with Non-Gaussian Spatial and Spatio-Temporal Data using FRK

Non-Gaussian spatial and spatial-temporal data are becoming increasingly...

0 Matthew Sainsbury-Dale, et al. ∙

∙ 07/09/2021

From Many to One: Consensus Inference in a MIP

A Model Intercomparison Project (MIP) consists of teams who each estimat...

0 Noel Cressie, et al. ∙

∙ 05/10/2021

Warped Gradient-Enhanced Gaussian Process Surrogate Models for Inference with Intractable Likelihoods

Markov chain Monte Carlo methods for intractable likelihoods, such as th...

0 Quan Vu, et al. ∙

∙ 02/08/2021

WOMBAT: A fully Bayesian global flux-inversion framework

WOMBAT (the WOllongong Methodology for Bayesian Assimilation of Trace-ga...

0 Andrew Zammit Mangion, et al. ∙

∙ 07/01/2020

Non-Homogeneous Poisson Process Intensity Modeling and Estimation using Measure Transport

Non-homogeneous Poisson processes are used in a wide range of scientific...

0 Tin Lok James Ng, et al. ∙

∙ 04/18/2020

Modeling Nonstationary and Asymmetric Multivariate Spatial Covariances via Deformations

Multivariate spatial-statistical models are useful for modeling environm...

0 Quan Vu, et al. ∙

∙ 10/29/2019

Deep Integro-Difference Equation Models for Spatio-Temporal Forecasting

Integro-difference equation (IDE) models describe the conditional depend...

27 Andrew Zammit Mangion, et al. ∙

∙ 07/17/2019

Multi-Scale Process Modelling and Distributed Computation for Spatial Data

Recent years have seen a huge development in spatial modelling and predi...

0 Andrew Zammit Mangion, et al. ∙

∙ 06/06/2019

Deep Compositional Spatial Models

Nonstationary, anisotropic spatial processes are often used when modelli...

0 Andrew Zammit Mangion, et al. ∙

∙ 05/15/2019

False Discovery Rates to Detect Signals from Incomplete Spatially Aggregated Data

There are a number of ways to test for the absence/presence of a spatial...

0 Hsin-Cheng Huang, et al. ∙

∙ 11/21/2017

On statistical approaches to generate Level 3 products from satellite remote sensing retrievals

Satellite remote sensing of trace gases such as carbon dioxide (CO_2) ha...

0 Andrew Zammit Mangion, et al. ∙

∙ 11/21/2017

On statistical approaches to generate Level 3 products from remote sensing retrievals

Remote sensing of trace gases such as carbon dioxide (CO_2) has led to a...

0 Andrew Zammit Mangion, et al. ∙

∙ 05/17/2013

Sparse Approximate Inference for Spatio-Temporal Point Process Models

Spatio-temporal point process models play a central role in the analysis...

0 Botond Cseke, et al. ∙