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Three characteristics (volume, duration, and peak intensity) are modeled by a multivariate distribution specified by three marginal distributions and a dependence structure via copula.
First, the multivariate distribution of the asset returns is fitted by a multivariate distribution.
Therefore, the distribution of market returns is fitted by a multivariate distribution.
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Multiple imputation by chained equations was used, which assumes a multivariate distribution exists without specifying its form.
Instead, we are examining the second moment of a multivariate distribution.
Since the number of samples is usually limited in this training set, a model is chosen beforehand: The generation of this set is modeled as drawing samples from a random process P x), where the distribution of this random process is approximated by a multivariate normal distribution N μ, Σ.
The emission distribution of gene expression in each state is modeled by a multivariate Gaussian distribution whose dimension equals the number of genes used by the classifier.
Additionally, in order to circumvent the noise typically associated with microarray data, the gene expressions were approximated by a multivariate Gaussian distribution and the regression weights were estimated by minimizing the regression error that was averaged over the Gaussian distribution.
Although the EMVC algorithm does not have specific distributional requirements, sources of genomic data are often well represented by a multivariate normal distribution, especially after appropriate transformations.
The scenarios set is described by a multivariate normal distribution generated via a sampling technique with symmetric correlation matrix.
Each class is described by a multivariate Gaussian distribution with an n-dimensional mean vector (the equivalent of class centres) and a variance-covariance structure (a measure of within class spread).
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