A Bayesian Parameter Estimation Approach to Response Surface Optimization in Quality Engineering

Year 2019, Volume: 23 Issue: 5, 767 - 774, 01.10.2019

Elif Kozan Onur Köksoy

https://doi.org/10.16984/saufenbilder.485785

Cited By: 2

Abstract

ABSTRACT

In
recent years, Bayesian analyses have become increasingly popular for solving
industrial related problems. This paper illustrates the use of Bayesian methods
in response surface methodology (RSM) in the context of “off-line quality”
improvement. Bayesian linear regression uses the prior information in the high
uncertainty state of the response function to make more efficient and more
realistic inferences than can be obtained with classical regression. Several
different models of parameter estimation and uncertainty analysis will be
presented for comparative purposes. An example illustrates the findings.

 

Keywords

Response surface methodology, Bayesian regression

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