Kriging Uncertainty near a Contextual Decision Boundary
Abstract
Stochastic kriging and contextual Gaussian-process allocation relate observations across input locations. Their useful comparison targets local choices, separating observation noise from uncertainty about the surface. Direct checks near action reversals can reveal failures hidden by average prediction error. Posterior precision remains conditional on the covariance representation rather than protection against every context shift. This focused synthesis develops an empirical comparison that preserves the terminal action, observation process, and total verification cost. Its proposed tests are prospective; no new simulation run, model-training result, or intervention effect is reported.
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