Probability Density Decomposition for Conditionally Dependent Random Variables Modeled by Vines
Annals of Mathematics and Artificial Intelligence
Using Optimal Dependency-Trees for Combinational Optimization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Combinatonal Optimization by Learning and Simulation of Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
An Introduction to Copulas (Springer Series in Statistics)
An Introduction to Copulas (Springer Series in Statistics)
The equation for response to selection and its use for prediction
Evolutionary Computation
Estimation of distribution algorithm based on archimedean copulas
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Graphical Models in Applied Multivariate Statistics
Graphical Models in Applied Multivariate Statistics
Estimation of distribution algorithm based on copula theory
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Supervised probabilistic classification based on Gaussian copulas
MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
Dependence trees with copula selection for continuous estimation of distribution algorithms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Estimation of distribution algorithms based on copula functions
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Selecting and estimating regular vine copulae and application to financial returns
Computational Statistics & Data Analysis
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A new Estimation of Distribution Algorithm is presented. The proposed algorithm, called D-vine EDA, uses a graphical model which is based on pair copula decomposition. By means of copula functions it is possible to model the dependence structure in a joint distribution with marginals of different type. Thus, this paper introduces the D-vine EDA and performs experiments and statistical tests to assess the best algorithm. The set of experiments shows the potential of the D-vine EDA