AI RESEARCH
Spatio-temporal probabilistic forecast using MMAF-guided learning
arXiv CS.LG
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ArXi:2603.15055v1 Announce Type: cross We employ stochastic feed-forward neural networks with Gaussian-distributed weights to determine a probabilistic forecast for spatio-temporal raster datasets. The networks are trained using MMAF-guided learning, a generalized Bayesian methodology in which the observed data are preprocessed using an embedding designed to produce a low-dimensional representation that captures their dependence and causal structure.