Optimal design of sea-walls requires the extreme value analysis of a variety of oceanographic data. Asymptotic arguments suggest the use of multivariate extreme value models, but empirical studies ...
In the recent years, there has been a growing interest in proposing covariance models for multivariate Gaussian random fields. Some of these covariance models are very flexible and can capture both ...
Abstract: In recent years, utilizing data from the evolutionary process of multiobjective evolutionary algorithms (MOEAs) to learn knowledge and guide evolutionary search has become a popular research ...
TSD 20: Multivariate meta-analysis of summary data for combining treatment effects on correlated outcomes and evaluating surrogate endpoints (PDF, 1.2MB) – October 2019 – Updated December 2022: ...
The Multivariate Gaussian model is a classic workhorse for classification tasks. Given an image, it can predict its class by calculating which statistical distribution the image most likely belongs to ...
Abstract: The heavy-tailed Multivariate Normal Inverse Gaussian (MNIG) distribution is a recent variance-mean mixture of a multivariate Gaussian with a univariate inverse Gaussian distribution. Due to ...
ABSTRACT: Precipitation is a critical meteorological factor that significantly impacts agriculture in the sub-Saharan and Sahelian regions of Africa. Accurate knowledge of precipitation levels aids in ...
Let ϕ be an arbitrary generalized Gaussian (squeezed coherent state), Λ_{αβ}=(α_1 Z ×···× α_nℤ)×(β_1ℤ×···\timesβ_nℤ)$ a rectangular lattice ...
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