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首页> 外文期刊>International Journal of Production Research >Markov chain Monte Carlo in Bayesian models for testing gamma and lognormal S-type process qualities
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Markov chain Monte Carlo in Bayesian models for testing gamma and lognormal S-type process qualities

机译：贝叶斯模型中的马尔可夫链蒙特卡罗，用于测试伽玛和对数正态S型过程质量

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摘要

The process capability index C-pu is widely used to measure S-type process quality. Many researchers have presented adaptive techniques for assessing the true C-pu assuming normality. However, the quality characteristic is often abnormal, and the derived techniques based on the normality assumption could mislead the manager into making uninformed decisions. Therefore, this study provides an alternative method for assessing C-pu of non-normal processes. The Markov chain Monte Carlo, an emerging popular statistical tool, is integrated into Bayesian models to seek the empirical posterior distributions of specific gamma and lognormal parameters. Afterwards, the lower credible interval bound of C-pu can be derived for testing the non-normal process quality. Simulations show that the proposed method is adaptive and has good performance in terms of coverage probability.
机译：工艺能力指数C-pu被广泛用于衡量S型工艺质量。许多研究人员提出了用于评估假设正常的真实C-pu的自适应技术。但是，质量特征通常是异常的，基于正态性假设的派生技术可能会误导经理做出无知的决策。因此，本研究提供了一种评估非正常过程C-pu的替代方法。马尔可夫链蒙特卡洛（Monte Carlo）是一种新兴的流行统计工具，已集成到贝叶斯模型中，以寻找特定伽玛和对数正态参数的经验后验分布。然后，可以得出C-pu的较低可信区间界限，以测试非正常过程质量。仿真表明，该方法具有自适应性，在覆盖概率上具有良好的性能。

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