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.
Birincil Dil | İngilizce |
---|---|
Bölüm | Araştırma Makalesi |
Yazarlar | |
Yayımlanma Tarihi | 1 Ekim 2019 |
Gönderilme Tarihi | 20 Kasım 2018 |
Kabul Tarihi | 19 Mart 2019 |
Yayımlandığı Sayı | Yıl 2019 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.