Inference for spatial extremal dependence models can be computationally
...
Models with random effects, such as generalised linear mixed models (GLM...
The last decade has seen an explosion in data sources available for the
...
Normalizing flows are objects used for modeling complicated probability
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The natural cycles of the surface-to-atmosphere fluxes of carbon dioxide...
Neural networks have recently shown promise for likelihood-free inferenc...
Deep neural network models have become ubiquitous in recent years, and h...
Spatial statistics is concerned with the analysis of data that have spat...
Understanding and predicting environmental phenomena often requires the
...
Recent years have seen an increased interest in the application of metho...
Flux inversion is the process by which sources and sinks of a gas are
id...
Bayesian methods for modelling and inference are being increasingly used...
Non-Gaussian spatial and spatial-temporal data are becoming increasingly...
A Model Intercomparison Project (MIP) consists of teams who each estimat...
Markov chain Monte Carlo methods for intractable likelihoods, such as th...
WOMBAT (the WOllongong Methodology for Bayesian Assimilation of Trace-ga...
Non-homogeneous Poisson processes are used in a wide range of scientific...
Multivariate spatial-statistical models are useful for modeling environm...
Integro-difference equation (IDE) models describe the conditional depend...
Recent years have seen a huge development in spatial modelling and predi...
Nonstationary, anisotropic spatial processes are often used when modelli...
There are a number of ways to test for the absence/presence of a spatial...
Satellite remote sensing of trace gases such as carbon dioxide (CO_2) ha...
Remote sensing of trace gases such as carbon dioxide (CO_2) has led to a...
Spatio-temporal point process models play a central role in the analysis...