Post-Inference Model Chaining: A Problem Statement and a Model-based Bootstrap for Training End-to-End Digital Twins
A model-based bootstrap is proposed: an initial chain forwards fitted means, bootstrap chains are refitted on fixed-center bootstrap data while forwarding their mean prediction, and a target-stage future error enters a studentized prediction root, and results support improved endpoint prediction in the tested setting.
Lukas Madenach, Timo Bailer, Tobias Eilert
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