Supplemental Content A Bayesian optimization
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After the initialization, the sequential component of the optimization begins. At iteration t, all previously observed data y = y1:t at points X = X1:t is used to construct a probabilistic surrogate model s y,X. The next location xt+1 is determined by maximizing a chosen acquisition function which measures the benefit or utility associated with evaluating a proposed x 2 X . In this article, we restrict our focus to only considering expected improvement [11],
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