论文标题
有关Higgins-Tsokos损失功能，用于建模软件故障时间的贝叶斯可靠性分析
Bayesian Reliability Analysis of the Power Law Process with Respect to the Higgins-Tsokos Loss Function for Modeling Software Failure Times
论文作者
论文摘要
电力法过程，也称为非均匀泊松过程，已在各个方面使用，其中之一是软件可靠性评估。具体而言，通过使用其强度函数来计算软件可靠性的变化速率作为时变函数。使用真实数据显示了贝叶斯分析适用于功率法过程的理由。首先确定了最能表征强度函数的关键参数行为的概率分布，然后获得了基于Higgins-Tsokos损失函数的功率法律过程的基于可能性的贝叶斯可靠性估计。由于模拟研究和使用实际数据，贝叶斯估计显示与使用不同样本量的最大似然估计相比，表现出色。此外，进行了灵敏度分析，从而导致贝叶斯估计对先前的选择敏感。无论是参数还是非参数。
The Power Law Process, also known as Non-Homogeneous Poisson Process, has been used in various aspects, one of which is the software reliability assessment. Specifically, by using its intensity function to compute the rate of change of a software reliability as time-varying function. Justification of Bayesian analysis applicability to the Power Law Process was shown using real data. The probability distribution that best characterizes the behavior of the key parameter of the intensity function was first identified, then the likelihood-based Bayesian reliability estimate of the Power Law Process under the Higgins-Tsokos loss function was obtained. As a result of a simulation study and using real data, the Bayesian estimate shows an outstanding performance compared to the maximum likelihood estimate using different sample sizes. In addition, a sensitivity analysis was performed, resulting in the Bayesian estimate being sensitive to the prior selection; whether parametric or non-parametric.
