Combination of several predictors for forensic age estimation
Background Nearly all practical forensic age diagnostics studies estimate the probability distribution of the age conditional on the developmental status of a certain anatomical feature. Given such a probability distribution, the probability of a person exceeding a certain legal age threshold is computed. In court, forensic experts are often asked to summarize the probabilities obtained by evaluating different age indicators of the same person. Objective The present study demonstrates computation of the age probability distribution conditional on the conjunction of several different age indicators given the age probability distributions conditional on the development status of the respective single anatomical features. Material and methods Data from two distinctively different studies on age estimation were used to join their probability information via Bayes’ theorem. Each of the cited studies is based on the development status of only one of two different anatomical structures: third molar and clavicular epiphysis. Results We derive general formulae for Bayesian information joining in forensic age estimation. Posterior distributions of age class, given the simultaneous statuses of the two anatomical features are generated. Finally, the study presents the technique on an artificial case example. Conclusion Bayes’ theorem can be used in forensic age estimations to combine information from several different anatomical features to yield more precise probability values of age given development status data of several distinctly different anatomical features. Conditional stochastic independence of the single age indicators as used in our article has to be scrutinized and is not generally recommendable..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:34 |
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| Enthalten in: |
Rechtsmedizin - 34(2024), 2 vom: 03. Jan., Seite 115-121 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Hubig, Michael [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| BKL: | |
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| Themen: |
Age probability distribution |
| Anmerkungen: |
© The Author(s) 2024 |
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| doi: |
10.1007/s00194-023-00672-7 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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SPR055315062 |
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| 520 | |a Background Nearly all practical forensic age diagnostics studies estimate the probability distribution of the age conditional on the developmental status of a certain anatomical feature. Given such a probability distribution, the probability of a person exceeding a certain legal age threshold is computed. In court, forensic experts are often asked to summarize the probabilities obtained by evaluating different age indicators of the same person. Objective The present study demonstrates computation of the age probability distribution conditional on the conjunction of several different age indicators given the age probability distributions conditional on the development status of the respective single anatomical features. Material and methods Data from two distinctively different studies on age estimation were used to join their probability information via Bayes’ theorem. Each of the cited studies is based on the development status of only one of two different anatomical structures: third molar and clavicular epiphysis. Results We derive general formulae for Bayesian information joining in forensic age estimation. Posterior distributions of age class, given the simultaneous statuses of the two anatomical features are generated. Finally, the study presents the technique on an artificial case example. Conclusion Bayes’ theorem can be used in forensic age estimations to combine information from several different anatomical features to yield more precise probability values of age given development status data of several distinctly different anatomical features. Conditional stochastic independence of the single age indicators as used in our article has to be scrutinized and is not generally recommendable. | ||
| 650 | 4 | |a Forensic age estimation in the living |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Anatomical age markers |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Age probability distribution |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Joining age information |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Bayes theorem |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Wittschieber, Daniel |4 aut | |
| 700 | 1 | |a Hunold, Thomas |4 aut | |
| 700 | 1 | |a Muggenthaler, Holger |4 aut | |
| 700 | 1 | |a Schenkl, Sebastian |4 aut | |
| 700 | 1 | |a Mall, Gita |4 aut | |
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