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

Journal article (2023)

Authors

M.A. Mendoza Lugo Hydraulic Structures and Flood Risk - CEG
O. Morales Napoles Hydraulic Structures and Flood Risk - CEG
D. Paprotny Hydraulic Structures and Flood Risk - CEG
E. Ragno Hydraulic Structures and Flood Risk - CEG
Research Group
Hydraulic Structures and Flood Risk (CEG) (TU Delft)
Copyright: © 2023 M.A. Mendoza Lugo, O. Morales Napoles, D. Paprotny, P.J.P. Koot, E. Ragno
DOI: https://doi.org/10.1016/j.softx.2022.101279
More Info
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Published Date

2023

Language

English

Faculty

Civil Engineering & Geosciences

Department

Hydraulic Engineering

Research Group

Hydraulic Structures and Flood Risk

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.