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A collection of idioms for modeling activity level evaluations in forensic science
This paper presents a collection of idioms that is useful for modeling activity level evaluations in forensic science using Bayesian networks. The idioms are categorized into five groups: cause-consequence idioms, narrative idioms, synthesis idioms, hypothesis-conditioning idioms, and evidence-condi...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10276233/ https://www.ncbi.nlm.nih.gov/pubmed/37332325 http://dx.doi.org/10.1016/j.fsisyn.2023.100331 |
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author | Vink, M. Sjerps, M.J. |
author_facet | Vink, M. Sjerps, M.J. |
author_sort | Vink, M. |
collection | PubMed |
description | This paper presents a collection of idioms that is useful for modeling activity level evaluations in forensic science using Bayesian networks. The idioms are categorized into five groups: cause-consequence idioms, narrative idioms, synthesis idioms, hypothesis-conditioning idioms, and evidence-conditioning idioms. Each category represents a specific modeling objective. Furthermore, we support the use of an idiom-based approach and emphasize the relevance of our collection by combining several of the presented idioms to create a more comprehensive template model. This model can be used in cases involving transfer evidence and disputes over the actor and/or activity. Additionally, we cite literature that employs idioms in template models or case-specific models, providing the reader with examples of their use in forensic casework. |
format | Online Article Text |
id | pubmed-10276233 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-102762332023-06-18 A collection of idioms for modeling activity level evaluations in forensic science Vink, M. Sjerps, M.J. Forensic Sci Int Synerg Interdisciplinary Forensics This paper presents a collection of idioms that is useful for modeling activity level evaluations in forensic science using Bayesian networks. The idioms are categorized into five groups: cause-consequence idioms, narrative idioms, synthesis idioms, hypothesis-conditioning idioms, and evidence-conditioning idioms. Each category represents a specific modeling objective. Furthermore, we support the use of an idiom-based approach and emphasize the relevance of our collection by combining several of the presented idioms to create a more comprehensive template model. This model can be used in cases involving transfer evidence and disputes over the actor and/or activity. Additionally, we cite literature that employs idioms in template models or case-specific models, providing the reader with examples of their use in forensic casework. Elsevier 2023-05-12 /pmc/articles/PMC10276233/ /pubmed/37332325 http://dx.doi.org/10.1016/j.fsisyn.2023.100331 Text en © 2023 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Interdisciplinary Forensics Vink, M. Sjerps, M.J. A collection of idioms for modeling activity level evaluations in forensic science |
title | A collection of idioms for modeling activity level evaluations in forensic science |
title_full | A collection of idioms for modeling activity level evaluations in forensic science |
title_fullStr | A collection of idioms for modeling activity level evaluations in forensic science |
title_full_unstemmed | A collection of idioms for modeling activity level evaluations in forensic science |
title_short | A collection of idioms for modeling activity level evaluations in forensic science |
title_sort | collection of idioms for modeling activity level evaluations in forensic science |
topic | Interdisciplinary Forensics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10276233/ https://www.ncbi.nlm.nih.gov/pubmed/37332325 http://dx.doi.org/10.1016/j.fsisyn.2023.100331 |
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