PyBanshee version (1.0): A Python implementation of the MATLAB toolbox BANSHEE 
for Non-Parametric Bayesian Networks with updated features

Koot, Paul; Mendoza-Lugo, Miguel Angel; Paprotny, Dominik; Morales-Nápoles, Oswaldo; Ragno, Elisa; Worm, Daniël T.H.

doi:10.1016/j.softx.2022.101279

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PyBanshee version (1.0): A Python implementation of the MATLAB toolbox BANSHEE for Non-Parametric Bayesian Networks with updated features

Authors

Koot, Paul
External Organizations;

Mendoza-Lugo, Miguel Angel
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/persons/resource/Dominik.Paprotny

Paprotny, Dominik
Potsdam Institute for Climate Impact Research;

Morales-Nápoles, Oswaldo
External Organizations;

Ragno, Elisa
External Organizations;

Worm, Daniël T.H.
External Organizations;

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Citation

Koot, P., Mendoza-Lugo, M. A., Paprotny, D., Morales-Nápoles, O., Ragno, E., Worm, D. T. (2023): PyBanshee version (1.0): A Python implementation of the MATLAB toolbox BANSHEE for Non-Parametric Bayesian Networks with updated features. - SoftwareX, 21, 101279.
https://doi.org/10.1016/j.softx.2022.101279

Cite as: https://publications.pik-potsdam.de/pubman/item/item_27674

Abstract

In this paper we discuss PyBanshee, which is a Python-based open-source implementation of the MATLAB toolbox BANSHEE. PyBanshee constitutes the first fully open-source package to quantify, visualize and validate Non-Parametric Bayesian Networks (NPBNs). The architecture of PyBanshee is heavily based on its MATLAB predecessor. It presents the full implementation of existing tools and introduces new modules. Specifically, PyBanshee allows for: (i) choosing fully parametric one-dimensional margins, (ii) choosing different sample sizes for the model-validation tests based on the Hellinger distance, (iii) drawing user-defined sample sizes of the NPBN, (iv) sample-based conditioning sampling (similarly to the closed-source proprietary package UNINET by LightTwist Software) and (v) visualizing the comparison between the histograms of the unconditional and conditional marginal distributions. New detailed examples demonstrating new features are provided.