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STS551 Stephen Wu et al.
shrinking of the model parameters. This is because of the false assumption that
all data points are independent, while in fact, there is correlation within each
experiment. Therefore, hierarchical modeling is needed to properly capture the
model uncertainty, leading to reasonable decision-making. Here, we
demonstrate an efficient approximation for the computationally demanding
hierarchical model developed under practical concerns. This allows possible
application of hierarchical Bayesian model to a wide range of engineering
applications with complex models.

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