Development of Information-Driven Robust Design:A Bayesian View

摘要

The Bayesian approach is a proven method for formally incorporating scientific knowledge, expertise, and informed judgment into statistical analysis; however, there is room for improvement. The conventional approach considers a random sample to determine the mean and variance of the current system performance, yet there are other ways to determine this type of information. One such method is robust design, which attempts to determine the optimum operating conditions for a system. The techniques used in robust design that could improve the estimation of a posterior distribution include design of experiments, consideration of uncontrollable (noise) factors, and experiments in which restrictions and constraints are imposed. In this paper, we propose a robust design-based Bayesian model to estimate the mean and variance of a posterior distribution more precisely. The proposed model is illustrated through an example and is compared to the traditional Bayesian approach.