Abdel-Aty Yahia, Kayid Mohamed, Alomani Ghadah
Department of Mathematics, College of Science, Taibah University, Saudi Arabia.
Department of Mathematics, Faculty of Science, Al-Azhar University, Nasr City, 11884, Egypt.
Heliyon. 2024 Jul 6;10(13):e34087. doi: 10.1016/j.heliyon.2024.e34087. eCollection 2024 Jul 15.
A Bayesian method based on the learning rate parameter is called a generalized Bayesian method. In this study, joint hybrid censored type I and type II samples from exponential populations were examined to determine the influence of the parameter on the estimation results. To investigate the selection effects of the learning rate and the loss parameters on the estimation results, we considered two additional loss functions in the Bayesian approach: the linear and the generalized entropy loss functions. We then compared the generalized Bayesian algorithm with the traditional Bayesian algorithm. We performed Monte Carlo simulations to compare the performance of the estimation results with the losses and different values of . The effects of different losses with different values and learning rate parameters are examined using an example.
基于学习率参数的贝叶斯方法被称为广义贝叶斯方法。在本研究中，对来自指数总体的联合混合I型和II型删失样本进行了检验，以确定参数对估计结果的影响。为了研究学习率和损失参数对估计结果的选择效应，我们在贝叶斯方法中考虑了另外两个损失函数：线性损失函数和广义熵损失函数。然后，我们将广义贝叶斯算法与传统贝叶斯算法进行了比较。我们进行了蒙特卡罗模拟，以比较不同损失和不同值时估计结果的性能。通过一个例子检验了不同值和学习率参数下不同损失的影响。
