A Bayesian Optimal Design for Sequential Accelerated Degradation Testing

A Bayesian Optimal Design for Sequential Accelerated Degradation Testing

When optimizing an accelerated degradation testing (ADT) plan, the initial values of unknown model parameters must be pre-specified. However, it is usually difficult to obtain the exact values, since many uncertainties are embedded in these parameters. Bayesian ADT optimal design was presented to ad...

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Bibliographic Details
Main Authors: Xiaoyang Li, Yuqing Hu, Fuqiang Sun, Rui Kang
Format: Article
Language:English
Published: MDPI AG 2017-07-01
Series:Entropy
Subjects:
Bayesian optimal design
accelerated degradation testing (ADT)
sequential decision
D-optimality
inverse Gaussian process
Online Access:https://www.mdpi.com/1099-4300/19/7/325

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https://www.mdpi.com/1099-4300/19/7/325

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