Spatial Prediction with Guided Diffusion
Read the original on arXiv Statistics ML →Spatial prediction is a key task in analyses of spatially or spatio-temporally referenced data, typically handled with hierarchical models that combine a process model and a data model. The predictive distribution—conditional on observed data—is often intractable, but guided diffusion modeling from machine learning can sample from it when the likelihood is known and differentiable. The authors demonstrate this approach on Gaussian and non‑Gaussian processes, various data models (direct observations, aggregates, counts, censoring, area‑exceedance), validate against exact and MCMC results, and apply it to methane‑flux inversion over the UK and Ireland, showing accurate sampling, applicability to intractable MCMC cases, and good scalability with dimensionality.
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