11 citations · 19 across the 4 of their papers we have counts for
4 papers
Learning complex dependency structure of gene regulatory networks from high dimensional micro-array data with Gaussian Bayesian networks
Catharina Elisabeth Graafland, José Manuel Gutiérrez
Gene expression datasets consist of thousand of genes with relatively small samplesizes (i.e. are large--small-). Moreover, dependencies of various orders co-exist in the dat…
The Probabilistic Backbone of Data-Driven Complex Networks: An example in Climate
Catharina Graafland, José M. Gutiérrez, Juan M. López +2
Correlation Networks (CNs) inherently suffer from redundant information in their network topology. Bayesian Networks (BNs), on the other hand, include only non-redundant informatio…
Who Learns Better Bayesian Network Structures: Accuracy and Speed of Structure Learning Algorithms
Marco Scutari, Catharina Elisabeth Graafland, José Manuel Gutiérrez
Three classes of algorithms to learn the structure of Bayesian networks from data are common in the literature: constraint-based algorithms, which use conditional independence test…
Nonparametric Kernel Density Estimation for Univariate Curent Status Data
Bert van Es, Catharina Elisabeth Graafland
We derive estimators of the density of the event times of current status data. The estimators are derived for the situations where the distribution of the observation times is know…