Korteweg-de Vries Institute for Mathematics

Photographer: xxx

prof. dr. M.J. (Marjan) Sjerps


  • Faculty of Science
    Korteweg-de Vries Instituut
  • Visiting address
    Science Park 107
    Science Park 107  Room number: C3.134
  • Postal address:
    Postbus  94248
    1090 GE  Amsterdam
  • m.j.sjerps@uva.nl
    T: 0205256540
    T: 0205255217

2018

  • Sampat, A. A. S., van Daelen, B., Lopatka, M., Mol, H., van der Weg, G., Vivo-Truyols, G., ... 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). DOI: 10.3390/separations5030043 
  • 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. DOI: 10.1093/lpr/mgy006  [details] 

2017

  • de Zoete, J. C., Sjerps, M. J., & Meester, R. W. J. (2017). Evaluating evidence in linked crimes with multiple offenders. Science & justice, 57(3), 228-238. DOI: 10.1016/j.scijus.2017.01.003 
  • 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. DOI: 10.1016/j.forc.2016.10.003  [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. DOI: 10.1111/1556-4029.13339 
  • 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. DOI: 10.1016/j.forsciint.2017.03.011  [details] 

2016

  • 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. DOI: 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. DOI: 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. DOI: 10.1093/lpr/mgv005  [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. DOI: 10.1016/j.fsigen.2015.09.007  [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. DOI: 10.1016/j.foodchem.2016.01.134  [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. DOI: 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. DOI: 10.1016/j.fsigss.2015.09.121  [details] 
  • de Zoete, J., Sjerps, M., Lagnado, D., & Fenton, N. (2015). Modelling crime linkage with Bayesian networks. Science & justice, 55(3), 209-217. DOI: 10.1016/j.scijus.2014.11.005  [details] 

2014

  • 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. DOI: 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. DOI: 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. DOI: 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] 

2012

  • 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. DOI: 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. DOI: 10.1007/978-94-007-1433-5  [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. DOI: 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] 

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. [details] 

2011

  • 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] 

2011

  • 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. DOI: 10.1198/jasa.2011.br1106  [details] 
  • Sjerps, M. (2011). Bewijskracht 10, volle vaart recht vooruit. (Oratiereeks). Amsterdam: Universiteit van Amsterdam. [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.
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