Factorial Sampling Plans for Preliminary Computational Experiments
1991/05/01 by Max D. Morris · 3,982 citations
Computer Science · Decision Sciences · Mathematics · #Advanced Multi-Objective Optimization Algorithms #Algorithm #Computational complexity theory #Computational resource #Computer science #Factorial #Function (biology) #Mathematical optimization #Mathematics #Monotonic function #Optimal Experimental Design Methods #Probabilistic and Robust Engineering Design #Representation (politics) #Sample (material) #Sampling (signal processing)
paper · doi:10.1080/00401706.1991.10484804
published in Technometrics 33(2), 161-174 (Taylor & Francis)
openalex publication_date 1991/05/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
Abstract
A computational model is a representation of some physical or other system of interest, first expressed mathematically and then implemented in the form of a computer program; it may be viewed as a function of inputs that, when evaluated, produces outputs. Motivation for this article comes from computational models that are deterministic, complicated enough to make classical mathematical analysis impractical and that have a moderate-to-large number of inputs. The problem of designing computational experiments to determine which inputs have important effects on an output is considered. The proposed experimental plans are composed of individually randomized one-factor-at-a-time designs, and data analysis is based on the resulting random sample of observed elementary effects, those changes in an output due solely to changes in a particular input. Advantages of this approach include a lack of reliance on assumptions of relative sparsity of important inputs, monotonicity of outputs with respect to inputs, or adequacy of a low-order polynomial as an approximation to the computational model.
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