SPIN Unprocessed October 31, 2026 ai_technology research
Amortized Localized Posterior Inference for Model Calibration with Application to an Ocean Turbulence Closure
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arXiv:2610.37731v1 Announce Type: new Abstract: Model discrepancy may prevent a single calibration parameter from reproducing a physical response across the context domain. In addition, uncertainties in observations and contexts affect calibration and must be accounted for and quantified. We construct a family of localized posteriors by weighting observation likelihoods according to their proximity to a target context. Gaussian localization weights incorporate context uncertainty through covaria
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