Statistical deep learning for spatial and spatiotemporal data
Deep neural network models have become ubiquitous in recent years and have been
applied to nearly all areas of science, engineering, and industry. These models are …
applied to nearly all areas of science, engineering, and industry. These models are …
Neural methods for amortized inference
Simulation-based methods for statistical inference have evolved dramatically over the past
50 years, kee** pace with technological advancements. The field is undergoing a new …
50 years, kee** pace with technological advancements. The field is undergoing a new …
[HTML][HTML] Inertial navigation meets deep learning: A survey of current trends and future directions
Inertial sensing is employed in a wide range of applications and platforms, from everyday
devices such as smartphones to complex systems like autonomous vehicles. In recent years …
devices such as smartphones to complex systems like autonomous vehicles. In recent years …
Likelihood-free parameter estimation with neural Bayes estimators
Neural Bayes estimators are neural networks that approximate Bayes estimators. They are
fast, likelihood-free, and amenable to rapid bootstrap-based uncertainty quantification. In …
fast, likelihood-free, and amenable to rapid bootstrap-based uncertainty quantification. In …
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. Although they are appealing to use with spatial models, where …
and likelihood-free manner. Although they are appealing to use with spatial models, where …
A full‐scale operational digital twin for a water resource recovery facility—A case study of Eindhoven Water Resource Recovery Facility
Digital transformation for the water sector has gained momentum in recent years, and many
water resource recovery facilities modelers have already started transitioning from …
water resource recovery facilities modelers have already started transitioning from …
Artificial neural networks for model identification and parameter estimation in computational cognitive models
Computational cognitive models have been used extensively to formalize cognitive
processes. Model parameters offer a simple way to quantify individual differences in how …
processes. Model parameters offer a simple way to quantify individual differences in how …
Modeling of spatial extremes in environmental data science: Time to move away from max-stable processes
Environmental data science for spatial extremes has traditionally relied heavily on max-
stable processes. Even though the popularity of these models has perhaps peaked with …
stable processes. Even though the popularity of these models has perhaps peaked with …
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. Building on recent …
burdensome since they involve intractable and/or censored likelihoods. Building on recent …
[HTML][HTML] Neural likelihood surfaces for spatial processes with computationally intensive or intractable likelihoods
In spatial statistics, fast and accurate parameter estimation, coupled with a reliable means of
uncertainty quantification, can be challenging when fitting a spatial process to real-world …
uncertainty quantification, can be challenging when fitting a spatial process to real-world …