Regular vine distributions which constitute a flexible class of multivariate dependence
models are discussed. Since multivariate copulae constructed through pair-copula …

The series aims to: present current and emerging innovations in GIScience; describe new
and robust GIScience methods for use in transdisciplinary problem solving and decision …

Most of grading results obtained by the traditional rock burst prediction model are
qualitatively expressed, the corresponding relationship between the rock burst prediction …

This paper aims to investigate the impact of copula selection on geotechnical reliability
under incomplete probability information. The copula theory is introduced briefly. Thereafter …

Copulas are functions that couple marginal distribution in order to generate joint probability
distribution functions; in this way, they model the dependence among random variables …

Studying phenomena that follow a skewed distribution and entail an extremal behaviour is
important in many disciplines. How to describe and model the dependence of skewed …

This paper describes a new iterative procedure for probabilistic history matching using a
discrete Latin Hypercube (DLHC) sampling method and nonparametric density estimation …

Copulas are a flexible tool to model dependence of random variables. They cover the range
from perfect negative to positive dependence, include the independent case and incorporate …

This study proposes a novel method for assessing the time-variant reliability of bridge
structures by combining deep learning and regular vine (R-vine) copula models. The sample …

For improving the reliability analysis of bridge girders, a consideration of the nonlinear
dependence among multivariable random variables is essential. Thus, this study presents a …