11. The marginal distribution has a unit mean, has a positive support, and is independent of. 12. The main idea in the proof is the continuity of the mutual information in the pairwise marginal distribution . 13. Furthermore, let denote the cumulative distribution functions of the one-dimensional marginal distributions of, that means 14. Which has marginal distributions of the same type ( 3 ) and Pareto Type II univariate marginal distributions. 15. Which has marginal distributions of the same type ( 3 ) and Pareto Type II univariate marginal distributions . 16. Thus, forecasting with Monte-Carlo simulation with the Gaussian copula and well-specified marginal distributions are effective. 17. The measurement is repeated again a large number of times and a marginal distribution is retrieved from the current difference. 18. Quantitative techniques that use Monte-Carlo simulation with the Gaussian copula and well-specified marginal distributions are effective. 19. The marginal distribution of X is also approximated by creating a histogram of the X coordinates without consideration of the Y coordinates. 20. Thus, the elements corresponding to X in the above partial sweeping equation represent the marginal distribution of X in potential form.
