14 citations · 18 across the 4 of their papers we have counts for
5 papers
On the connection between cherry-tree copulas and truncated R-vine copulas
Edith Kovács, Tamás Szántai
Vine copulas are a flexible way for modeling dependences using only pair-copulas as building blocks. However if the number of variables grows the problem gets fast intractable. For…
Discovering the Markov network structure
Edith Kovács, Tamás Szántai
In this paper a new proof is given for the supermodularity of information content. Using the decomposability of the information content an algorithm is given for discovering the Ma…
Vine copulas as a mean for the construction of high dimensional probability distribution associated to a Markov Network
Edith Kovacs, Tamas Szantai
Building higher-dimensional copulas is generally recognized as a difficult problem. Regular-vines using bivariate copulas provide a flexible class of high-dimensional dependency mo…
Discovering a junction tree behind a Markov network by a greedy algorithm
Tamas Szantai, Edith Kovacs
In an earlier paper we introduced a special kind of k-width junction tree, called k-th order t-cherry junction tree in order to approximate a joint probability distribution. The ap…
Multivariate Copula Expressed by Lower Dimensional Copulas
Edith Kovacs, Tamas Szantai
Modeling of high order multivariate probability distribution is a difficult problem which occurs in many fields. Copula approach is a good choice for this purpose, but the curse of…