Partition Precision under Uncertain Simulation Inputs
Abstract
A partitioned regression can estimate outputs precisely under one driving distribution while the input uncertainty moves its feasibility boundary. The evaluation separates new input observations from new simulator replications and directly checks boundary candidates. More replication can sharpen the nominal approximation without resolving whether it represents the relevant operating environment. 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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