Papers › Ordinal Optimisation for the Gaussian Copula Model
Ordinal Optimisation for the Gaussian Copula Model
Robert Chin, Jonathan E. Rowe, Iman Shames, Chris Manzie, Dragan Nešić
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We present results on the estimation and evaluation of success probabilities for ordinal optimisation over uncountable sets (such as subsets of ℝᵈ). Our formulation invokes an assumption of a Gaussian copula model, and we show that the success probability can be equivalently computed by assuming a special case of additive noise. We formally prove a lower bound on the success probability under the Gaussian copula model, and numerical experiments demonstrate that the lower bound yields a reasonable approximation to the actual success probability. Lastly, we showcase the utility of our results by guaranteeing high success probabilities with ordinal optimisation.
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