The omnipotent instrument for modeling multivariate dependence of random variables in standard probability theory has become copulas discovered by A. Sklar in 1959. Only recently Omladič and Stopar prove that in the bivariate case an analogous role is played by exactly the same copulas for random variables coming from finitely additive probability spaces. One of the main results of this paper is that this is true also in the general multivariate case. The extension to n dimensions requires a better understanding of quasi-copulas, the lattice closure of copulas with respect to the pointwise order. We need to develop a new equivalent definition of this notion that should be useful in other applications as well. Another tool we introduce and seems to be new even in the standard probability approach, is multivariate quasi-distributions. We also expand the coherence theory for quasi-copulas and quasi-distributions to the multivariate situation. Finally, our main result is a multivariate Sklar type theorem in the imprecise setting.

标准概率论中用于对随机变量的多元相关性进行建模的万能工具已成为 A. Sklar 在 1959 年发现的 copulas。直到最近 Omladič 和 Stopar 才证明在双变量情况下，完全相同的 copulas 对随机变量的到来起到了类似的作用从有限可加概率空间。本文的主要结果之一是，在一般多变量情况下也是如此。对n的扩展维度需要更好地理解准 copulas，即 copula 相对于逐点顺序的晶格闭合。我们需要为这个概念开发一个新的等效定义，它在其他应用程序中也应该有用。我们引入的另一个工具，即使在标准概率方法中似乎也是新的，它是多元准分布。我们还将准系词和准分布的相干理论扩展到多变量情况。最后，我们的主要结果是不精确设置中的多元 Sklar 类型定理。