article · 2022
Comparison of Estimation Accuracy between Bayesian D-Optimization Method and SenTest Method
Liming Li, Jingxin Zhang, Xiao Xia Yuan
Cited by 0Abstract and details only
Abstract
In order to compare the valuation accuracy of Bayesian D optimization method (DS method) and SenTest method, the DS method sensitivity experimental design study was carried out, and the experimental calculation process was established. The algorithm and valuation accuracy of Bayesian D optimization method and SenTest method are studied by simulation method. The results show that under the small sample size, the DS method can quickly obtain the mixing interval and quickly obtain the mean. At medium sample sizes, the SenTest method is more robust. Therefore, the DS method is more dependent on the prior information, and in the case of unfamiliar product parameters, the DS method is not advantageous to estimate the P response point.
Cite this paper
Li, L., Zhang, J., & Yuan, X. X. (2022). Comparison of Estimation Accuracy between Bayesian D-Optimization Method and SenTest Method. https://doi.org/10.1109/icica56942.2022.00017
Li, Liming, et al. “Comparison of Estimation Accuracy between Bayesian D-Optimization Method and SenTest Method.” vol. 22, 2022, pp. 54–57, https://doi.org/10.1109/icica56942.2022.00017.
Li, Liming, Jingxin Zhang, and Xiao Xia Yuan. 2022. “Comparison of Estimation Accuracy between Bayesian D-Optimization Method and SenTest Method.” https://doi.org/10.1109/icica56942.2022.00017.
Li, L., Zhang, J. and Yuan, X.X. (2022) ‘Comparison of Estimation Accuracy between Bayesian D-Optimization Method and SenTest Method’. Available at: https://doi.org/10.1109/icica56942.2022.00017.
@article{li2022comparison,
title = {{Comparison of Estimation Accuracy between Bayesian D-Optimization Method and SenTest Method}},
author = {Li, Liming and Zhang, Jingxin and Yuan, Xiao Xia},
volume = {22},
pages = {54--57},
year = {2022},
doi = {10.1109/icica56942.2022.00017}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.