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