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首页> 外文会议>2017 International Conference on Sensing, Diagnostics, Prognostics, and Control >Prior Distribution Selection Criterion in Accelerated Degradation Testing Bayesian Optimization Design Based on Bayes Factors
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Prior Distribution Selection Criterion in Accelerated Degradation Testing Bayesian Optimization Design Based on Bayes Factors

机译：基于贝叶斯因素的加速降解试验贝叶斯优化设计中的先验分布选择准则

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摘要

Accelerated degradation testing (ADT) Bayesian optimization design can determine prior distribution of model parameters by prior information, which avoiding the uncertainty to the result of design brought by assuming parameter selection arbitrarily. However, different people may have different tendencies when choosing prior distributions through the same prior information. It may result in different schemes of optimization design. Hence, the challenge how to obtain prior distribution through prior information effectively is urgent to be solved. The use of Bayes factors is a method of Bayesian model comparison in classical method of hypothesis testing. It quantifies the support for a model over another on the basis of observed data. This kind of technical definition of "support" can be reference to compare prior distributions in ADT Bayesian optimization design. This article proposes a criterion of prior distribution selection in ADT Bayesian optimization design based on Bayes factors, which provides a new path for choosing prior distribution.
机译：加速退化测试（ADT）贝叶斯优化设计可以通过先验信息确定模型参数的先验分布，从而避免了任意假设参数选择所带来的设计结果不确定性。但是，通过相同的先验信息选择先验分布时，不同的人可能会有不同的倾向。这可能会导致优化设计的方案不同。因此，迫切需要解决如何有效地通过先验信息获得先验分配的难题。贝叶斯因子的使用是经典假设检验方法中的贝叶斯模型比较方法。它根据观察到的数据来量化对模型的支持。这种“支持”的技术定义可以用来比较ADT贝叶斯优化设计中的先验分布。本文提出了一种基于贝叶斯因子的ADT贝叶斯优化设计中的先验分布选择准则，为选择先验分布提供了一条新途径。

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