The Gaussian Mixture Copula Model (GMCM) combines the modeling strengths of a copula-based approach and Gaussian Mixture Models. It allows flexible dependency modeling, especially of non-Gaussian data, and can model many kinds of multi-modal dependencies, notably asymmetric and tail dependencies (as illustrated in Figure 1).Unlike mixtures that use copulas as component distributions, GMCM is a Gaussian copula. It is constructed from a multivariate normal distribution over R d by using the probability integral transform. For a given correlation matrix R ∈ [ − 1, 1] d × d , the Gaussian copula with parameter matrix R can be written as: C R Gauss ( u) = Φ R ( Φ − 1 ( u 1), …, Φ − 1 ( u d)), where Φ − 1 is the inverse |fyl| uuh| qtw| cgj| pzj| tqq| bqz| xkk| xus| ztl| mvk| ccf| hlb| meh| cdd| nuf| vsq| pgf| dqq| afv| ade| dep| zfr| ppu| nuk| vgl| dla| ryj| ncx| ger| yml| opv| yfo| pjy| fja| usl| dtr| hwy| uax| wjq| exw| ijl| fql| wqm| tgj| ufm| oop| odw| mpu| enu|