BANSHEE–A MATLAB toolbox for Non-Parametric Bayesian Networks

Dominik Paprotny*, Oswaldo Morales-Nápoles, Daniël T.H. Worm, Elisa Ragno

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
90 Downloads (Pure)

Abstract

Bayesian Networks (BNs) are probabilistic, graphical models for representing complex dependency structures. They have many applications in science and engineering. Their particularly powerful variant – Non-Parametric BNs – are for the first time implemented as an open-access scriptable code, in the form of a MATLAB toolbox “BANSHEE”.1 The software allows for quantifying the BN, validating the underlying assumptions of the model, visualizing the network and its corresponding rank correlation matrix, and finally making inference with a BN based on existing or new evidence. We also include in the toolbox, and discuss in the paper, some applied BN models published in most recent scientific literature.

Original languageEnglish
Article number100588
Pages (from-to)1-7
Number of pages7
JournalSoftwareX
Volume12
DOIs
Publication statusPublished - 2020

Keywords

  • Belief Nets
  • Copulas
  • Probabilistic models

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