本研究的主要是針對製程能力指標Cpm進行研究，在此利用貝氏統計方法建立Cpm的信賴下限，此信賴下限為伽瑪分配的函數。透過貝氏統計方法所建立的Cpm信賴下限與其他三組方法所建立的Cpm信賴下限進行比較。第一組為利用傳統抽樣分配理論所建立的Cpm信賴下限，其為非中卡方分配的函數；另外兩組Cpm信賴下限則分別為卡方分配與常態分配的函數。在此透過不同參數的設定對此四組Cpm的信賴下限進行比較，結果顯示在某些參數的設定下，利用貝氏統計方法所建立的Cpm信賴下限較有利於其他三組Cpm信賴下限。

　　　　　The main focus of this paper is on a particular type of process capability indices, termed Cpm in the quality control community, enabling to consider proximity of the process center to the pre-specified target value as well as process inherent variability altogether whilst investigating process capability. In order to take the probabilistic properties of the point estimator used on Cpm into account, the key element of this study is to present a generalized Bayes-type approach that allows for the construction of a lower interval limit for the process measure Cpm. In comparison to the classical analogue for Cpm (built upon the traditional frequentist theory) entailing a non-central chi-square distribution that the practitioners are generally unfamiliar with, the Bayesian method relates simply to the integration of a gamma distribution. The lower Bayesian interval estimate on Cpm is also compared with the other three lower confidence bounds posed in the literature and then various experimental studies under a number of process parameter configurations assumed are conducted to illustrate practical guidance about the types of processes for which the Bayesian-based procedure might be advantageous. Directions of how to choose adequate lower confidence limits on Cpm for practical use are reported as well.