BOIS linearizes known composite objective functions around Gaussian process predictions to compute closed-form uncertainty estimates, and benchmarks show it matches or beats MC-BO and OP-BO on two process design problems with lower acquisition cost.
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On the Implementation of a Bayesian Optimization Framework for Interconnected Systems
BOIS linearizes known composite objective functions around Gaussian process predictions to compute closed-form uncertainty estimates, and benchmarks show it matches or beats MC-BO and OP-BO on two process design problems with lower acquisition cost.