{"ID":2870318,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.12884","arxiv_id":"2509.12884","title":"Modeling nonstationary spatial processes with normalizing flows","abstract":"Nonstationary spatial processes can often be represented as stationary processes on a warped spatial domain. Selecting an appropriate spatial warping function for a given application is often difficult and, as a result of this, warping methods have largely been limited to two-dimensional spatial domains. In this paper, we introduce a novel approach to modeling nonstationary, anisotropic spatial processes using neural autoregressive flows (NAFs), a class of invertible mappings capable of generating complex, high-dimensional warpings. Through simulation studies we demonstrate that a NAF-based model has greater representational capacity than other commonly used spatial process models. We apply our proposed modeling framework to a subset of the 3D Argo Floats dataset, highlighting the utility of our framework in real-world applications.","short_abstract":"Nonstationary spatial processes can often be represented as stationary processes on a warped spatial domain. Selecting an appropriate spatial warping function for a given application is often difficult and, as a result of this, warping methods have largely been limited to two-dimensional spatial domains. In this paper,...","url_abs":"https://arxiv.org/abs/2509.12884","url_pdf":"https://arxiv.org/pdf/2509.12884v2","authors":"[\"Pratik Nag\",\"Andrew Zammit-Mangion\",\"Ying Sun\"]","published":"2025-09-16T09:37:18Z","proceeding":"stat.ME","tasks":"[\"stat.ME\",\"stat.ML\"]","methods":"[]","has_code":false}