Statistical deep learning for spatial and spatiotemporal data
CK Wikle, A Zammit-Mangion - Annual Review of Statistics and …, 2023 - annualreviews.org
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 …
Statistical deep learning for spatial and spatio-temporal data
CK Wikle, A Zammit-Mangion - arXiv preprint arXiv:2206.02218, 2022 - arxiv.org
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 …
Scalable Bayesian transport maps for high-dimensional non-Gaussian spatial fields
M Katzfuss, F Schäfer - Journal of the American Statistical …, 2024 - Taylor & Francis
A multivariate distribution can be described by a triangular transport map from the target
distribution to a simple reference distribution. We propose Bayesian nonparametric …
distribution to a simple reference distribution. We propose Bayesian nonparametric …
Mixture modeling with normalizing flows for spherical density estimation
TLJ Ng, A Zammit-Mangion - Advances in Data Analysis and Classification, 2024 - Springer
Normalizing flows are objects used for modeling complicated probability density functions,
and have attracted considerable interest in recent years. Many flexible families of …
and have attracted considerable interest in recent years. Many flexible families of …