Vergeer, P., Alberink, I., Sjerps, M., & Ypma, R. (2020). Why calibrating LR-systems is best practice. A reaction to “The evaluation of evidence for microspectrophotometry data using functional data analysis”, in FSI 305. Forensic Science International, 314, [110388]. https://doi.org/10.1016/j.forsciint.2020.110388[details]
de Koeijer, J. A., Sjerps, M. J., Vergeer, P., & Berger, C. E. H. (2020). Combining evidence in complex cases: a practical approach to interdisciplinary casework. Science and Justice, 60(1), 20-29. https://doi.org/10.1016/j.scijus.2019.09.001[details]
Zuidberg, M., Bettman, M., Aarts, L. H. J., Sjerps, M., & Kokshoorn, B. (2019). Targeting relevant sampling areas for human biological traces: Where to sample displaced bodies for offender DNA? Science and Justice, 59(2), 153-161. https://doi.org/10.1016/j.scijus.2018.10.002[details]
2018
Sampat, A. A. S., van Daelen, B., Lopatka, M., Mol, H., van der Weg, G., Vivó-Truyols , G., Sjerps, M., Schoenmakers, P. J., & van Asten, A. C. (2018). Detection and Characterization of Ignitable Liquid Residues in Forensic Fire Debris Samples by Comprehensive Two-Dimensional Gas Chromatography. Separations, 5(3), [43]. https://doi.org/10.3390/separations5030043[details]
de Zoete, J., & Sjerps, M. (2018). Combining multiple pieces of evidence using a lower bound for the LR. Law, probability and risk, 17(2), 163-178. https://doi.org/10.1093/lpr/mgy006[details]
2017
Alberink, I., Bolck, A., Sjerps, M., & Vergeer, P. (2017). Comment to “A guideline for the validation of likelihood ratio methods used for forensic evidence evaluation”. Forensic Science International, 276, 154. https://doi.org/10.1016/j.forsciint.2017.03.011[details]
Leegwater, A. J., Meuwly, D., Sjerps, M. J., Vergeer, P., & Alberink, I. (2017). Performance Study of a Score-based Likelihood Ratio System for Forensic Fingermark Comparison. Journal of Forensic Sciences, 62(3), 626-640. https://doi.org/10.1111/1556-4029.13339[details]
Lopatka, M., Sampat, A. A., Jonkers, S., Adutwum, L. A., Mol, H. G. J., van der Weg, G., ... Vivó-Truyols, G. (2017). Local Ion Signatures (LIS) for the examination of comprehensive two-dimensional gas chromatography applied to fire debris analysis. Forensic Chemistry, 3, 1-13. https://doi.org/10.1016/j.forc.2016.10.003[details]
Lopatka, M., Barcaru, A., Sjerps, M. J., & Vivó-Truyols, G. (2016). Leveraging probabilistic peak detection to estimate baseline drift in complex chromatographic samples. Journal of Chromatography A, 1431, 122-130. https://doi.org/10.1016/j.chroma.2015.12.063[details]
Sampat, A., Lopatka, M., Sjerps, M., Vivo-Truyols, G., Schoenmakers, P., & van Asten, A. (2016). Forensic potential of comprehensive two-dimensional gas chromatography. Trends in Analytical Chemistry, 80, 345-363. https://doi.org/10.1016/j.trac.2015.10.011[details]
Sjerps, M. J., Alberink, I., Bolck, A., Stoel, R. D., Vergeer, P., & van Zanten, J. H. (2016). Uncertainty and LR: to integrate or not to integrate, that’s the question. Law, probability and risk, 15(1), 23-29. https://doi.org/10.1093/lpr/mgv005[details]
de Rijke, E., Schoorl, J. C., Cerli, C., Vonhof, H. B., Verdegaal, S. J. A., Vivó-Truyols, G., ... de Koster, C. G. (2016). The use of δ2H and δ18O isotopic analyses combined with chemometrics as a traceability tool for the geographical origin of bell peppers. Food Chemistry, 204, 122-128. https://doi.org/10.1016/j.foodchem.2016.01.134[details]
de Zoete, J., Curran, J., & Sjerps, M. (2016). A probabilistic approach for the interpretation of RNA profiles as cell type evidence. Forensic Science International. Genetics, 20, 30-44. https://doi.org/10.1016/j.fsigen.2015.09.007[details]
2015
Lopatka, M., Sigman, M. E., Sjerps, M. J., Williams, M. R., & Vivó-Truyols, G. (2015). Class-conditional feature modeling for ignitable liquid classification with substantial substrate contribution in fire debris analysis. Forensic Science International, 252, 177-186. https://doi.org/10.1016/j.forsciint.2015.04.035[details]
de Zoete, J., Curran, J., & Sjerps, M. (2015). Categorical methods for the interpretation of RNA profiles as cell type evidence and their limitations. Forensic Science International. Genetics Supplement Series, 5, e305-e307. https://doi.org/10.1016/j.fsigss.2015.09.121[details]
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[details]
Lopatka, M., Vivó-Truyols, G., & Sjerps, M. J. (2014). Probabilistic peak detection for first-order chromatographic data. Analytica Chimica Acta, 817, 9-16. https://doi.org/10.1016/j.aca.2014.02.015[details]
de Zoete, J., Sjerps, M., Meester, R., & Cator, E. (2014). The combined evidential value of autosomal and Y-chromosomal DNA profiles obtained from the same sample. International Journal of Legal Medicine, 128(6), 897-904. https://doi.org/10.1007/s00414-014-0971-7[details]
de Zoete, J., Vriend, K., Dolman, M., Meester, R., & Sjerps, M. (2014). Het gebruik van schakelbewijs; juridische en kans-theoretische gezichtspunten. Expertise en Recht, 2014(5), 153-167. [details]
2013
Haraksim, R., Meuwly, D., Doekhie, G., Vergeer, P., & Sjerps, M. (2013). Assignment of the evidential value of a fingermark general pattern using a Bayesian network. In A. Brömme, & C. Busch (Eds.), BIOSIG 2013: proceedings of the 12th International Conference of the Biometrics Special Interest Group : 04.-06. September 2013 in Darmstadt, Germany (pp. 99-109). (GI-Edition : Lecture notes in informatics ; Vol. 212). Bonn: Gesellschaft für Informatik. [details]
Berger, C. E. H., & Sjerps, M. J. (2012). Discussion paper: Reaction to Hamer and Thompson in LPR. Law, probability and risk, 11(4), 373-375. https://doi.org/10.1093/lpr/mgs024[details]
Bolck, A., Stoel, R., Alberink, I., & Sjerps, M. J. (2012). LR models for evidence evaluation. Chinese Journal of Forensic Sciences, 4, 28-42. [details]
Sjerps, M. J., & Berger, C. E. H. (2012). How clear is transparent? Reporting expert reasoning in legal cases. Law, probability and risk, 11(4), 317-329. https://doi.org/10.1093/lpr/mgs017[details]
Stoel, R. D., & Sjerps, M. (2012). Interpretation of Forensic Evidence. In S. Roeser, R. Hillerbrand, P. Sandin, & M. Peterson (Eds.), Handbook of risk theory: epistemology, decision theory, ethics, and social implications of risk (pp. 135-158). Dordrecht: Springer. https://doi.org/10.1007/978-94-007-1433-5[details]
2011
van der Beek, C. P., Kloosterman, A. D., & Sjerps, M. J. (2011). De detectie van vals positieve en de preventie van vals negatieve matches bij grootschalige DNA-databankvergelijkingen. Expertise en Recht, 2011(6), 219-221. [details]
2014
Harris, H. A., Sjerps, M. J., Kloosterman, A. D., Quak, A., & Geradts, Z. J. (2014). Framework for Registration, Classification, and evaluation of errors in the Forensic DNA Typing Process. Proceedings of the American Academy of Forensic Sciences, 20, 19. [W13]. [details]
van der Peijl, G. J. Q., & Sjerps, M. J. (2011). Combination of evidence in complex casework using Bayesian networks. Proceedings of the American Academy of Forensic Sciences, 17, 133-134. [details]
2015
Berger, C., & Sjerps, M. (2015). International Conference on Forensic Inference and Statistics 2014. Expertise en Recht, 2015(3), 96-97. [details]
2012
Sjerps, M. (2012). Bewijskracht 10, volle vaart recht vooruit. (Oratiereeks; No. 417). Amsterdam: Universiteit van Amsterdam. [details]
Sjerps, M. J. (2011). [Review of: D.H. Kaye (2010) The double helix and the law of evidence]. Journal of the American Statistical Association, 106(494), 769. https://doi.org/10.1198/jasa.2011.br1106[details]
Sjerps, M., & Berger, C. (2011). Het Bayesiaanse model biedt een helder zicht op een complexe werkelijkheid. Den Haag: Nederlands Forensisch Instituut. [details]
2010
Sjerps, M., & Kloosterman, A. (2010). Het gebruik van Bayesiaanse netwerken in de forensische (DNA-)statistiek. Ars Aequi, 59(7), 502-508. [details]
Sjerps, M., Kloosterman, A., & van der Beek, K. (2010). De interpretatie van een DNA-databankmatch. Delikt en Delinkwent, 40(2), 138-155. [details]
2016
Stols-Witlox, M. J. N., Sjerps, M. J., Hendriks, E., Wallert, A., van Tilborgh, J. L., de Zoete, J. C., & Hermens, E. (2016). Scientific Reasoning in Art: Evaluating Evidence in Paintings Research using a Bayesian Approach. Poster session presented at NICAS 2016 projects presentations, Amsterdam, Netherlands.
De UvA maakt gebruik van cookies en daarmee vergelijkbare technieken voor het functioneren, meten en optimaliseren van de website. Ook worden er cookies geplaatst om bijv. YouTube filmpjes te kunnen tonen en voor marketingdoeleinden. Deze laatste categorie betreffen de tracking cookies. Uw internetgedrag kan worden gevolgd door middel van deze tracking cookies. Door op “Accepteer alle cookies” te klikken gaat u hiermee akkoord. Lees ook het UvA Privacy statement
Noodzakelijk
Cookies noodzakelijk voor het basisfunctioneren van de website. Deze cookies worden bijvoorbeeld ingezet om het inloggen voor studenten en medewerkers mogelijk te maken.
Noodzakelijk & Optimalisatie
Cookies die worden geplaatst om anoniem gegevens te verzamelen over het gebruik van de website om deze te verbeteren.
Noodzakelijk & Optimalisatie & Marketing
Cookies die in staat stellen bezoekers te volgen en van gepersonaliseerde advertenties te voorzien. Externe advertentienetwerken verzamelen individuele gegevens over internetgedrag. Selecteer deze categorie om YouTube video's te kunnen kijken.