AI RESEARCH
LatticeVision: Image to Image Networks for Modeling Non-Stationary Spatial Data
arXiv CS.LG
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ArXi:2505.09803v3 Announce Type: replace-cross In many applications, we wish to fit a parametric statistical model to a small ensemble of spatially distributed random variables ('fields'). However, parameter inference using maximum likelihood estimation (MLE) is computationally prohibitive, especially for large, non-stationary fields. Thus, many recent works train neural networks to estimate parameters given spatial fields as input, sidestepping MLE completely. In this work we focus on a popular class of parametric, spatially autoregressive (SAR) models.