Contextual Gaussian Processes and Correlated Beliefs before Application
Abstract
Both methods allow observations in one setting to inform another, making dependence assumptions part of the contextual decision. Evaluation should inspect sparse contexts where the two representations recommend different actions. Freezing the offline fit and obtaining direct local checks distinguishes useful information sharing from confidence created mainly by an untested relationship. 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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