The coefficient of variation is a useful indicator for comparing the spread of values between dataset with different units or widely different means. In this paper we address the problem of investigating the equality of the coefficients of variation from two independent populations. In order to do this we rely on the Bayesian Discrepancy Measure recently introduced in the literature. Computing this Bayesian measure of evidence is straightforward when the coefficient of variation is a function of a single parameter of the distribution. In contrast, it becomes difficult when it is a function of more parameters, often requiring the use of MCMC methods. We calculate the Bayesian Discrepancy Measure by considering a variety of distributions whose coefficients of variation depend on more than one parameter. We consider also applications to real data. As far as we know, some of the examined problems have not yet been covered in the literature.

翻译：变异系数是一种有用的指标，用于比较不同单位或均值差异较大的数据集之间的离散程度。本文研究检验两个独立总体的变异系数是否相等的问题。为此，我们采用文献中近期提出的贝叶斯差异度量（Bayesian Discrepancy Measure）。当变异系数是分布单一参数的函数时，该贝叶斯证据度量的计算较为直接；反之，当它是多个参数的函数时，计算变得困难，通常需要使用MCMC方法。我们通过考虑多种变异系数依赖于多个参数的分布来计算贝叶斯差异度量，并同时对实际数据进行了应用分析。据我们所知，所研究的某些问题尚未在文献中得到充分探讨。