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Software . 2021
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Software . 2021
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Software . 2021
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PyGModels: A Python package for exploring Probabilistic Graphical Models with Graph Theoretical Structures

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 12 May 2021Publisher:Zenodo
Authors: Doğu Kaan ERASLAN;

doi: 10.5281/zenodo.4489961 , 10.5281/zenodo.4751740

PyGModels: A Python package for exploring Probabilistic Graphical Models with Graph Theoretical Structures

Abstract

This version of the package is approved by the Journal of Open Source Software reviewers, and editors. It is the JOSS approved release.

Related Organizations
Keywords

inference, random variable, factor, probabilistic graphical models, partially directed graphical models, pgm, markov network, conditional random field, mixed graph, lwf chain graphs

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
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