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Abstract
Aim: The aim of Part III of this three-part study is to present – in Hungarian and through concrete examples – the basic principles of Bayesian network analysis used for activity-level evaluation of biological traces (including DNA) associated with criminal offenses.
Methodology: For the preparation of this study, the author reviewed international literature, professional recommendations, and conducted Bayesian network analyses.
Findings: An excellent method for the activity-level forensic probabilistic evaluation of trace materials examined in criminal cases is Bayesian network analysis. A Bayesian network (BN) is a directed acyclic graph that contains probabilistic variables and their conditional dependencies. The graph consists of entities (nodes, vertices) and the connections (edges) defined between them. The directed nature of a BN perfectly corresponds to the cause → effect or action → consequence dependencies (i.e., activity → material transfer) that are also the focus of activity-level analyses in forensics. A BN is acyclic, meaning that the unidirectional chain of nodes – representing propositions, analyzed activities, the associated material transfer (TPPR) events, and expert examination results – cannot return to any previous node. This accurately models the spatial and temporal dynamics of the change in traces, which can never revert to their initial state or point in time. The state and probability of each node are influenced by the state and probability of its directly connected parent node(s) (e.g., DNA transfer). The likelihoods of observing the trace evidence, given the prosecution’s and defense’s activity-level propositions, can be calculated from the probabilities of all nodes within the BN. Bayesian network analyses of an Australian and a Hungarian criminal case illustrate how this approach can support jurisdiction by providing statistical assessments of the probative value of forensic genetic results at the activity level.
Value: This is the first study to introduce this field in Hungarian to stakeholders in the justice system, providing both the professional framework and the terminology needed for domestic application. To the author’s knowledge, this is the first published application of Bayesian network analysis for activity-level forensic genetics in a Hungarian criminal case.
References
Cook, R., Evett, I. W., Jackson, G., Jones, P. J., Lambert, J. A. (1998). A model for case assessment and interpretation. Sci Justice., 38(3), 151–6. https://doi.org/10.1016/s1355-0306(98)72099-4
Daly, D. J., Murphy, C., McDermott, S. D. (2012). The transfer of touch DNA from hands to glass, fabric and wood. Forensic Science International: Genetics, 6(1), 41–46. https://doi.org/10.1016/j.fsigen.2010.12.016
Dobos, Á., Dudás-Boda, E., Füredi, S., Heinrich, A., Kormos, Z., Mátrai N.,
Pamzsav H., Tapasztó A. (2024). Genetikai Szakértői Intézet. In Forenzikus Füzetek 2. A Nemzeti Szakértői és Kutató Központ jelene és jövőképe II. (pp. 28–56). https://nszkk.gov.hu/content/forenzikus_fuzetek/ff-2024-2szam-full.pdf
Fullár, A., Kutnyánszky, V., Leiner, N. (2020). Identification of burglars using foil impressioning based on tool marks and DNA evidence. Forensic Sci Int., 316, 110524. https://doi.org/10.1016/j.forsciint.2020.110524
Füredi S. (2023). A nyomhordozók és emberi eredetű biológiai anyagmaradványok típusai és genetikai vizsgálatának jelentősége a magyarországi büntető- és közigazgatási eljárásokban – Esetleírások. (2023). Belügyi Szemle, 71(12), 2179–2196. https://doi.org/10.38146/BSZ.2023.12.4
Füredi S. (2024). A magyarországi bűnügyi DNS-profil nyilvántartás találatkeresési módszerének fejlesztése. Rendőrségi Tanulmányok 7 (Különszám), 3-77. http://dx.doi.org/10.53304/RT.2024.ksz.01
Füredi S. (2026a). A biológiai anyagmaradványok cselekvési szintű vizsgálata bűnügyekben I. - A DNS-azonosítás szintet lép. Belügyi Szemle, 74(4), 909-949. https://doi.org/10.38146/bsz-ajia-ajia.2026.v74.i4.pp909-949
Füredi S. (2026b). A biológiai anyagmaradványok cselekvési szintű vizsgálata bűnügyekben II. - A DNS-transzfer. Belügyi Szemle, 74(5), 1331-1368. https://doi.org/10.38146/bsz-ajia.2026.v74.i5.pp1331-1368
Gill, P., Hicks, T., Butler, J. M., Connolly, E., Gusmão, L., Kokshoorn, B., Morling, N., van Oorschot, R. A. H., Parson, W., Prinz, M., Schneider, P. M., Sijen T., & Taylor, D. (2020). DNA commission of the International society for forensic genetics: Assessing the value of forensic biological evidence - Guidelines highlighting the importance of propositions. Part II: Evaluation of biological traces considering activity level propositions. Forensic Science International: Genetics, 44, 102186. https://doi.org/10.1016/j.fsigen.2019.102186
Gill, P., Fonneløp, A., E., Hicks, T., X Xenophontos, S., Cariolou, M., van Oorschot, R., Buckel, I., Sukser, V., Papić, S., Merkaš, S., Kostic, A., Pereira, A. M., Teutsch, C., Forsberg, C., Haas, C., Petkovski, E., Hass, F., Masek, J., Stosic, J., Lee, J. S., Syn C. K-C, Groombridge, L., Trimborn, M., Hadjivassiliou, M., Breathnach, M., Novackova, J., Parson, W., Hatzer-Grubwieser, P., Pietikäinen, S., Joas, S., Willuweit, S., Grethe, S., Milićević, T., Hasselqvist, T., Kallupurackal, V., Stenzl, V., Jansson, S., Glocker, I., Brunck, S., Nyhagen, K., Lingelem, A. B. D., Autere, H., Thornbury, D., Pedersen, N., Fox, S., Moore, D., Escott, G., Petersen, C. B., Larsen, H. J., Giles, R., Allen, P. S., Prieto, L., Ramirez, E., de Yuso, I. M., Bastisch, I. (2025a). The ReAct project: Analysis of data from 23 different laboratories to characterise DNA recovery given two sets of activity level propositions. Forensic Science International: Genetics, 76, 103222. https://doi.org/10.1016/j.fsigen.2025.103222
Gill, P., Hicks, T., Kokshoorn, B., van Oorschot, R. A. H., Taylor D., Parson, W. (2026). Minimum FSI: Genetics requirements for publishing data on DNA transfer and recovery, given activities. Forensic Science International: Genetics, 80, 103330. https://doi.org/10.1016/j.fsigen.2025.103330
Gosch, A., Courts, C. (2019). On DNA transfer: The lack and difficulty of systematic research and how to do it better. Forensic Science International: Genetics, 40, 24–36. https://doi.org/10.1016/j.fsigen.2019.01.012
Kloosterman, A., Sjerps, M., Quak, A. (2014). Error rates in forensic DNA analysis: Definition, numbers, impact and communication. Forensic Science International: Genetics, 12, 77–85. https://doi.org/10.1016/j.fsigen.2014.04.014
Orbán J. (2013). A Bayes-hálók rendészeti alkalmazhatóságának vizsgálata. Pécsi Határőr Tudományos Közlemények, XIV., 379–386. https://www.pecshor.hu/periodika/XIV/orbanj.pdf
Orbán J. (2014). Kriminalisztikai valószínűségi becslés Bayes-hálókkal. Magyar Rendészet, 14(4), 115–130. https://folyoirat.ludovika.hu/index.php/magyrend/article/view/3909/3167
Orbán J. (2015). A felderítés és a nyomozás támogatása bayesi módszerekkel. Pécsi Határőr Tudományos Közlemények, XVI., 169–174. https://www.pecshor.hu/periodika/XVI/orban.pdf
Orbán J. (2018). Bayes-hálók a bűnügyekben. PhD értekezés. Pécsi Tudományegyetem Állam- és Jogtudományi Karának Doktori Iskolája. https://pea.lib.pte.hu/server/api/core/bitstreams/5f6e0a6b-29cb-43af-bcce-c99fd1ce58ca/content
Orbán J. (2019). Vélelmek bizonyosságának növelése a büntetőeljárásban. Útkeresés a Bayes módszerben rejlő lehetőségek használata felé az Alaptörvény XXVI. cikkére figyelemmel. Szegedi Tudományegyetem. https://acta.bibl.u-szeged.hu/71311/1/szegedi_jogasz_doktorandusz_konf_002_235-245.pdf
Orbán J. (2022) Bayesian Networks in Law Enforcement. Belügyi Tudományos Tanács. https://bm-tt.hu/wp-content/uploads/2022/02/OrbanJ_Security_Challenges_in_the_21st_Century_web-42.pdf
Petrétei D. (2023). A szakértői „üzemmódok”. Pécsi Határőr Tudományos Közlemények. XXV. Pécs, 301–308. https://pecshor.hu/periodika/XXV/Petretei_David.pdf
Szkuta, B., Ansell, R., Boiso, L., Connolly, E., Kloosterman, A. D., Kokshoorn, B., McKenna, L. G., Steensma, K., van Oorschot, R. A. H. (2019). Assessment of the transfer, persistence, prevalence and recovery of DNA traces from clothing: An inter-laboratory study on worn upper garments. Forensic Science International: Genetics, 42, 56–68. https://doi.org/10.1016/j.fsigen.2019.06.011
Taylor, D. (2016). The evaluation of exclusionary DNA results: a discussion of issues in R v. Drummond. Law, Probability and Risk, 15(3), 175–197. https://doi.org/10.1093/lpr/mgw004
Taylor, D., Biedermann, A., Hicks, T, Champod, & C. (2018). A template for constructing Bayesian networks in forensic biology cases when considering activity level propositions. Forensic Science International: Genetics, 33, 136–146. https://doi.org/10.1016/j.fsigen.2017.12.006
Taylor, D. & Kokshoorn, B. (2023). Forensic DNA Trace Evidence Interpretation: Activity Level Propositions and Likelihood Ratios (1st ed.). CRC Press. https://doi.org/10.4324/9781003273189
Taylor, D., Volgin, L., Gill, P., Kokshoorn, B. (2025). Accounting for inter-laboratory DNA recovery data variability when performing evaluations given activities. Forensic Science International: Genetics, 78, 103283. https://doi.org/10.1016/j.fsigen.2025.103283
Vink, M., de Koeijer, J. A., Sjerps, M. J. (2024) A template Bayesian network for combining forensic evidence on an item with an uncertain relation to the disputed activities. Forensic Science International: Synergy (9), 100546. https://doi.org/10.1016/j.fsisyn.2024.100546
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