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Activity Number: 400 - Multiple Aspects of Bayesian Model Selection and Variable Selection in Linear and Nonlinear Models
Type: Topic Contributed
Date/Time: Wednesday, August 5, 2020 : 1:00 PM to 2:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #312768
Title: Bayesian Hierarchical Modeling for Process Optimization
Author(s): Min Wang* and Linhan Ouyang and Chanseok Park and Yan Ma and YiZhong Ma
Companies: The University of Texas at San Antonio and Nanjing University of Aeronautics and Astronautics and Pusan National University and Nanjing University of Science and Technology and Nanjing University of Science and Technology
Keywords: Quality management; Bayesian hierarchical modeling Modeling; SUR models; Process optimization
Abstract:

Many industrial process optimization methods rely on empirical models that relate output responses to a set of design variables. One of the most crucial problems in process optimization is how to efficiently implement model selection and model estimation . This paper presents a Bayesian hierarchical modeling approach to process optimization based on the seemingly unrelated regression (SUR) models . This approach can estimate a set of predictors to be included in a model based on a Bayesian hierarchical procedure (i.e., model selection) and then give model prediction based on a Bayesian SUR model (i.e., model Meanwhile, a two stage optimization strategy considering practitioners preference information is proposed in process optimization , which initially finds a set of non dominated input setting s and then determines the best one based on the similarity to an ideal solution method. The performance and effectiveness of the proposed approach is illustrated with both simulation studies and a case study. The comparison results demonstrate that the proposed approach can be a good alternative to existing process optimization methods.


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