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cegpy : modelling with chain event graphs in Python
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Walley, Gareth, Shenvi, Aditi, Strong, Peter and Kobalczyk, Katarzyna (2023) cegpy : modelling with chain event graphs in Python. Knowledge-Based Systems, 274 . 110615. doi:10.1016/j.knosys.2023.110615 ISSN 0950-7051.
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Official URL: http://dx.doi.org/10.1016/j.knosys.2023.110615
Abstract
Chain event graphs (CEGs) are a recent family of probabilistic graphical models that generalise the popular Bayesian networks (BNs) family. Crucially, unlike BNs, a CEG is able to embed, within its graph and its statistical model, asymmetries exhibited by a process. These asymmetries might be in the conditional independence relationships or in the structure of the graph and its underlying event space. Structural asymmetries are common in many domains, and can occur naturally (e.g. a defendant vs prosecutor’s version of events) or by design (e.g. a public health intervention). Whilst two CEG packages exist in R for modelling processes with asymmetric conditional independencies, there currently exists no software that allows a user to leverage the theoretical developments of the CEG model family in modelling processes with structural asymmetries. In this paper, we present cegpy: the first Python implementation of CEGs and the first across all languages to support structurally asymmetric processes. cegpy contains an implementation of Bayesian learning and probability propagation algorithms for CEGs. We illustrate the functionality of cegpy using a structurally asymmetric dataset.
| Item Type: | Journal Article | |||||||||
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| Subjects: | Q Science > QA Mathematics | |||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Research Centres > Centre for Complexity Science Faculty of Science, Engineering and Medicine > Science > Mathematics Faculty of Science, Engineering and Medicine > Science > Statistics |
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| Library of Congress Subject Headings (LCSH): | Trees (Graph theory), Asymmetric synthesis, Python (Computer program language), Graphical modeling (Statistics), Mathematical statistics -- Graphic methods -- Research, Probabilities, Bayesian statistical decision theory | |||||||||
| Journal or Publication Title: | Knowledge-Based Systems | |||||||||
| Publisher: | Elsevier | |||||||||
| ISSN: | 0950-7051 | |||||||||
| Official Date: | 15 August 2023 | |||||||||
| Dates: |
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| Volume: | 274 | |||||||||
| Article Number: | 110615 | |||||||||
| DOI: | 10.1016/j.knosys.2023.110615 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||
| Date of first compliant deposit: | 27 June 2023 | |||||||||
| Date of first compliant Open Access: | 27 June 2023 | |||||||||
| RIOXX Funder/Project Grant: |
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