TY - JOUR
T1 - BANSHEE–A MATLAB toolbox for Non-Parametric Bayesian Networks
AU - Paprotny, Dominik
AU - Morales-Nápoles, Oswaldo
AU - Worm, Daniël T.H.
AU - Ragno, Elisa
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Belief Nets
KW - Copulas
KW - Probabilistic models
UR - http://www.scopus.com/inward/record.url?scp=85092089789&partnerID=8YFLogxK
U2 - 10.1016/j.softx.2020.100588
DO - 10.1016/j.softx.2020.100588
M3 - Article
AN - SCOPUS:85092089789
SN - 2352-7110
VL - 12
SP - 1
EP - 7
JO - SoftwareX
JF - SoftwareX
M1 - 100588
ER -