The most entropy distribution can be estimated based on some proper intended constraints. It is unbiased and unique on some constraints and is applied to find out the distribution of a data set. But it is difficult to get the distribution of multivariate data by saving the dependency between its variables and transferring it to the outcome distribution. On the other hand, copula function keeps safe the dependency. Thus entropy principle can be added to copula concept to approximate the distributions of the multivariate dependence data sets. In this article, we would like to work on the most copula entropy based on known Spearman’s rho as an extra constraint. For this aim, we approximate Spearman’s rho via empirical distribution function of a given data set. Then the most copula entropy based on Tsallis definition is gotten and compared with Shannon results in some senses by plots. Finally its density function is practically obtained.

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