A methodology for constructing narrative Bayesian networks for the evaluation of forensic fibre evidence given activity level propositions
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| Publicado en: | Forensic Science International (Online) vol. 376 (Nov 2025) |
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| Autor principal: | |
| Otros Autores: | , |
| Publicado: |
Elsevier Limited
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 022 | |a 0379-0738 | ||
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| 024 | 7 | |a 10.1016/j.forsciint.2025.112586 |2 doi | |
| 035 | |a 3246084784 | ||
| 045 | 2 | |b d20251101 |b d20251130 | |
| 084 | |a 170334 |2 nlm | ||
| 100 | 1 | |a Lau, Victoria | |
| 245 | 1 | |a A methodology for constructing narrative Bayesian networks for the evaluation of forensic fibre evidence given activity level propositions | |
| 260 | |b Elsevier Limited |c Nov 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The evaluation of forensic fibre evidence given activity level propositions is complex, due to the circumstances and factors of consideration in each case. While Bayesian Networks (BNs) are increasingly recognised for their potential in supporting this evaluative process, their application within the fibre and microtrace specialties remains limited, often relying on complex representations. This paper presents a simplified methodology for constructing narrative BNs for the activity-level evaluation of forensic fibre findings. Through an illustrative case scenario, we develop three examples of BNs designed as an accessible starting point for practitioners to build case-specific networks. These examples emphasise the transparent incorporation of case information, facilitate the assessment of the evaluation’s sensitivity to variations in data, and highlight avenues for further research. Significantly, the qualitative, narrative offers a format that is easier for both experts and the Court to understand, enhances user-friendliness and accessibility, and aligns with successful approaches in other forensic disciplines as forensic biology. This alignment has the potential to readily facilitate interdisciplinary collaboration and ultimately a more holistic approach. | |
| 653 | |a Qualitative analysis | ||
| 653 | |a Accessibility | ||
| 653 | |a Principles | ||
| 653 | |a Probability | ||
| 653 | |a Bayesian analysis | ||
| 653 | |a Networks | ||
| 653 | |a Forensic sciences | ||
| 653 | |a Forensic science | ||
| 653 | |a Forensic computing | ||
| 653 | |a Interdisciplinary aspects | ||
| 653 | |a Sensitivity analysis | ||
| 700 | 1 | |a Spindler, Xanthe | |
| 700 | 1 | |a Roux, Claude | |
| 773 | 0 | |t Forensic Science International (Online) |g vol. 376 (Nov 2025) | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3246084784/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3246084784/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3246084784/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |