过程优化的贝叶斯层次模型(Bayesian hierarchical modelling for process optimization)
发布时间: 2022-03-24 浏览次数: 10

报告摘要:

Empirical models that relate multiple quality features to a set of design variables play a vital role in many industrial process optimization methods. Many of the current modeling methods employ a single-response model to analyze industrial processes without taking into consideration the high correlations among the response variables and may result in a misleading prediction model, and therefore, poor process design. To deal with these issues, we first present a Bayesian hierarchical modelling approach to process optimization based on the seemingly unrelated regression (SUR) models. The proposed approach can estimate a set of predictors to be included in a model based on a Bayesian hierarchical procedure (i.e. model selection) and provide model prediction and optimization based on a Bayesian SUR model (i.e. model estimation). We then propose a robust version of Bayesian SUR models to simultaneously analyze multiple-feature systems while accounting for the high correlation, non-normality, and variable selection issues. Simulation experiments are executed to investigate the performance of the proposed Bayesian method, which is also illustrated by application to a laser cladding repair process. The analysis results show that the proposed modeling technique compares favorably with its classic counterpart in the literature.


报告人简介:

汪敏,美国德州大学圣安东尼奥分校 (University of Texas at San Antonio) 商学院管理科学与统计系副教授,博士生导师。20105月于美国克莱姆森大学(Clemson University)获得统计硕士学位;20135月于克莱姆森大学大学获得统计博士学位。20138- 201712月在美国密歇根理工大学数学科学系工作和在20178月破格提前提升为副教授并获得终身任期教授资格;现在在德州大学圣安东尼奥分校从事教学科研工作。近年来,先后参与和主持了美国自然科学基金委(NSF),密歇根交通部,以及美国卫生院(NIH)的研究课题。到目前为止,在各种期刊发表学术论文70多篇,其中最近研究成果发表在Bayesian AnalysisComputational Statistics & Data AnalysisComputers and Industrial Engineering, IISE Transactions, International Journal of Production Research, Journal of the Operational Research Society, Naval Research Logistics, The American Statistician 等。主要研究方向包括贝叶斯统计;计算统计;统计推断;质量和可靠性工程研究;高维数据分析和统计应用。



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