2019/08/14 by Hao Chen, William J. Welch, Chen, Hao +1
Computer Science · Decision Sciences · #Advanced Multi-Objective Optimization Algorithms #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimal Experimental Design Methods #Probabilistic and Robust Engineering Design
paper · pdf · doi:10.48550/arxiv.1908.05357
openalex publication_date 2019/08/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A computer code can simulate a system's propagation of variation from random\ninputs to output measures of quality. Our aim here is to estimate a critical\noutput tail probability or quantile without a large Monte Carlo experiment.\nInstead, we build a statistical surrogate for the input-output relationship\nwith a modest number of evaluations and then sequentially add further runs,\nguided by a criterion to improve the estimate. We compare two criteria in the\nliterature. Moreover, we investigate two practical questions: how to design the\ninitial code runs and how to model the input distribution. Hence, we close the\ngap between the theory of sequential design and its application.\n