Model-robust process optimization using Bayesian model averaging

Ramkumar Rajagopal, Enrique Del Castillo

Research output: Contribution to specialist publicationArticle

34 Scopus citations

Abstract

Traditional approaches for process optimization start by fitting a model and then optimizing the model to obtain optimal operating settings. These methods do not account for any uncertainty in the parameters of the model or in the form of the model. Bayesian approaches have been proposed recently to account for the uncertainty on the parameters of the model, assuming that the model form is known. This article presents a Bayesian predictive approach to process optimization that accounts for the uncertainty in the model form, also accounting for the uncertainty of the parameters given each potential model. We propose optimizing the model-averaged posterior predictive density of the response where the weighted average is taken using the model posterior probabilities as weights. The resulting model-robust optimization is illustrated with two experiments from the literature, one involving a mixture experiment and the other involving a small composite design.

Original languageEnglish (US)
Pages152-163
Number of pages12
Volume47
No2
Specialist publicationTechnometrics
DOIs
StatePublished - May 2005

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modeling and Simulation
  • Applied Mathematics

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