Finite-Time Information on a Kriging-Assisted Surface
Abstract
A kriging representation can borrow across locations while finite-time information analysis assumes a particular reward process. Evaluation should inspect that compatibility and directly check sparse favored regions. Independent complete runs provide uncertainty about terminal decisions. A low global prediction error does not make a normal-reward selection guarantee transferable to every correlated surface observation. 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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