Learning complex dependency structure of gene regulatory networks from high dimensional micro-array data with Gaussian Bayesian networks

no code implementations • 28 Jun 2021 • Catharina Elisabeth Graafland, José Manuel Gutiérrez

Gene expression datasets consist of thousand of genes with relatively small samplesizes (i. e. are large-$p$-small-$n$).

Who Learns Better Bayesian Network Structures: Accuracy and Speed of Structure Learning Algorithms

no code implementations • 30 May 2018 • 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 tests to learn the dependence structure of the data; score-based algorithms, which use goodness-of-fit scores as objective functions to maximise; and hybrid algorithms that combine both approaches.