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Found 10 papers, 7 papers with code
Neural Methods for Amortised Parameter Inference
Simulation-based methods for making statistical inference have evolved dramatically over the past 50 years, keeping pace with technological advancements.
Spatial Bayesian Neural Networks
We propose several variants of SBNNs, most of which are able to match the finite-dimensional distribution of the target process at the selected grid better than conventional BNNs of similar complexity.
Neural Bayes Estimators for Irregular Spatial Data using Graph Neural Networks
Neural Bayes estimators are neural networks that approximate Bayes estimators in a fast and likelihood-free manner.
Neural Bayes estimators for censored inference with peaks-over-threshold models
Making inference with spatial extremal dependence models can be computationally burdensome since they involve intractable and/or censored likelihoods.
Likelihood-Free Parameter Estimation with Neural Bayes Estimators
Neural point estimators are neural networks that map data to parameter point estimates.
Statistical Deep Learning for Spatial and Spatio-Temporal Data
Deep neural network models have become ubiquitous in recent years, and have been applied to nearly all areas of science, engineering, and industry.
Spherical Poisson Point Process Intensity Function Modeling and Estimation with Measure Transport
Recent years have seen an increased interest in the application of methods and techniques commonly associated with machine learning and artificial intelligence to spatial statistics.
Emulation of greenhouse-gas sensitivities using variational autoencoders
We show that our CVAE-based emulator outperforms the more traditional emulator built using empirical orthogonal functions and that it can be used with different LPDMs.
Deep Integro-Difference Equation Models for Spatio-Temporal Forecasting
Both procedures tend to be excellent for prediction purposes over small time horizons, but are generally time-consuming and, crucially, do not provide a global prior model for the temporally-varying dynamics that is realistic.
Deep Compositional Spatial Models
Spatial processes with nonstationary and anisotropic covariance structure are often used when modelling, analysing and predicting complex environmental phenomena.
