2019/11/05 by Robert Chin, Chin, Robert, Jonathan E. Rowe +7
Computer Science · Decision Sciences · Engineering · #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Optimization and Control (math.OC) #Probability (math.PR) #Reservoir Engineering and Simulation Methods #Simulation Techniques and Applications
paper · pdf · doi:10.48550/arxiv.1911.01993
openalex publication_date 2019/11/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.