Neural autoregressive flows enable flexible high-dimensional spatial warpings for nonstationary anisotropic processes, with simulations showing greater representational capacity than standard models and an application to 3D Argo Floats data.
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Introduces spSSA extending SSA to spatial data via three generalized eigenvalue procedures and a data augmentation method to estimate nonstationary subspace dimension.
New smooth invertible parameterization of anisotropic GF correlation length and diffusion matrix, with PC priors that penalize finite range and nonzero anisotropy for constant-parameter models.
citing papers explorer
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Modeling nonstationary spatial processes with normalizing flows
Neural autoregressive flows enable flexible high-dimensional spatial warpings for nonstationary anisotropic processes, with simulations showing greater representational capacity than standard models and an application to 3D Argo Floats data.
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Stationary subspace analysis for spatial data
Introduces spSSA extending SSA to spatial data via three generalized eigenvalue procedures and a data augmentation method to estimate nonstationary subspace dimension.
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A parameterization of anisotropic Gaussian fields with penalized complexity priors
New smooth invertible parameterization of anisotropic GF correlation length and diffusion matrix, with PC priors that penalize finite range and nonzero anisotropy for constant-parameter models.