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06/27/2023
Likelihood-free neural Bayes estimators for censored inference with peaks-over-threshold models
Inference for spatial extremal dependence models can be computationally ...
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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...
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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 ...
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01/16/2023
Mixture Modeling with Normalizing Flows for Spherical Density Estimation
Normalizing flows are objects used for modeling complicated probability ...
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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...
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08/27/2022
Fast Optimal Estimation with Intractable Models using Permutation-Invariant Neural Networks
Neural networks have recently shown promise for likelihood-free inferenc...
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06/05/2022
Statistical Deep Learning for Spatial and Spatio-Temporal Data
Deep neural network models have become ubiquitous in recent years, and h...
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02/08/2022
Basis-Function Models in Spatial Statistics
Spatial statistics is concerned with the analysis of data that have spat...
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02/07/2022
Constructing Large Nonstationary Spatio-Temporal Covariance Models via Compositional Warpings
Understanding and predicting environmental phenomena often requires the ...
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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...
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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...
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12/20/2021
A Review of Bayesian Modelling in Glaciology
Bayesian methods for modelling and inference are being increasingly used...
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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...
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07/09/2021
From Many to One: Consensus Inference in a MIP
A Model Intercomparison Project (MIP) consists of teams who each estimat...
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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...
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02/08/2021
WOMBAT: A fully Bayesian global flux-inversion framework
WOMBAT (the WOllongong Methodology for Bayesian Assimilation of Trace-ga...
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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...
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04/18/2020
Modeling Nonstationary and Asymmetric Multivariate Spatial Covariances via Deformations
Multivariate spatial-statistical models are useful for modeling environm...
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10/29/2019
Deep Integro-Difference Equation Models for Spatio-Temporal Forecasting
Integro-difference equation (IDE) models describe the conditional depend...
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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...
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06/06/2019
Deep Compositional Spatial Models
Nonstationary, anisotropic spatial processes are often used when modelli...
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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...
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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...
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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...
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05/17/2013
