Abstract

In this paper, the application of Bayesian methods for probability of detection (POD) estimation and the model-assisted probability of detection methodology is explored. A demonstration of Bayesian estimation for an eddy current POD evaluation case study is presented and compared with conventional approaches. Hierarchical Bayes models are introduced for estimating parameters including random variables in physics-based models. Results are presented that demonstrate the feasibility of simultaneously estimating model calibration parameters, model random variables and measurement error.

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