Sentence examples for multivariate distributions with from inspiring English sources

We conclude this section by noting the availability of multivariate distributions with Pareto conditionals rather than Pareto marginals.

The copula-based technique is a powerful method in modeling non-Gaussian multivariate distributions with non-linear inter-dimensional dependency.

Copulas provide the basis for estimation and application of multivariate distributions, with the relevant beta distributions of input values providing the marginal distributions.

The paper by Conway (1979) also discussed some additional properties and derived appropriate relationships for such systems, while a method of constructing multivariate distributions with specified univariate marginals and a given correlation matrix was studied by Cuadras (1992).

Within the graphical models context, data are considered as coming from Gaussian multivariate distributions with a structured concentration matrix (inverse of the covariance matrix), which reflects dependencies among variables.

Each column represents one 'market scenario', meaning one simulation from the multivariate distribution with the 'market' consisting of the N assets.

For example, Dutta and Genton (2014) gave a construction of a non-Gaussian multivariate distribution with all lower-dimensional Gaussians and considered some generalizations of this for a certain class of elliptical and skew-elliptical distributions.

where is the normal multivariate distribution with mean vector and covariance matrix, is the number of clusters, and is the prior probability that the observation has been generated by cluster.

The multivariate Shewhart control chart assumes that data is generated from a multivariate distribution with target mean μ and covariance ∑.

Pyne et al. [ 24] use a finite mixture of skew and heavy-tailed multivariate distributions fitted with an EM algorithm and validated the approach on lymphoblastic cell lines and regulatory T cells.

The observation densities are mixtures of five multivariate Gaussian distributions with diagonal covariance matrices.