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

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

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 Urheber:
Koot, Paul1, Autor
Mendoza-Lugo, Miguel Angel1, Autor
Paprotny, Dominik2, Autor              
Morales-Nápoles, Oswaldo1, Autor
Ragno, Elisa1, Autor
Worm, Daniël T.H.1, Autor
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Zusammenfassung: 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.

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Sprache(n): eng - Englisch
 Datum: 2021-10-192022-11-212022-12-082023-02
 Publikationsstatus: Final veröffentlicht
 Seiten: 5
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1016/j.softx.2022.101279
Organisational keyword: RD3 - Transformation Pathways
PIKDOMAIN: RD3 - Transformation Pathways
Working Group: Data-Centric Modeling of Cross-Sectoral Impacts
MDB-ID: No data to archive
Research topic keyword: Extremes
Research topic keyword: Complex Networks
Model / method: Quantitative Methods
OATYPE: Gold Open Access
 Art des Abschluß: -

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Titel: SoftwareX
Genre der Quelle: Zeitschrift, SCI, Scopus, oa
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Seiten: - Band / Heft: 21 Artikelnummer: 101279 Start- / Endseite: - Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/softwarex
Publisher: Elsevier