COMPUTATIONAL MODEL VALIDATION UNDER UNCERTAINTY

Abstract eng:
This paper proposes a new method to assess the validity of large-scale computational models by combining system reliability concepts with a Bayesian model validation approach. The concept of Bayesian hypothesis testing is extended to system-level problems where full-scale testing is impossible. This paper shows how component-level validation results may be used to derive a system-level validation measure. This derivation depends on the knowledge of inter-relationships between component modules. The proposed methods are demonstrated for series and parallel systems.

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