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Bayesian networks with forensic and other applications


Status Archive
School Computer and Information Science (CIS)
Division STIMA
Owner Anders Nordgaard

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Course plan

No of lectures

Preliminary 16 (including supervised exercises)

Recommended for

PhD students within areas of forensic, natural and medical science, who need training in probabilistic reasoning and decision theory.

The course was last given


The course provides understanding of basic concepts and teaches skills employed in the statistical evaluation of physical evidence. Forensic evidence will be used as examples. However, the students are strongly encouraged to bring projects/problems applicable to Bayesian network analysis also from other scientific areas such as medical genetics, ecology, evolutionary biology and clinical medicine.

Upon completion of the course, the participants should be able to:
- use knowledge about basic probability models and graphical network analysis for evaluation of physical evidence (eg forensic and medical genetics, (forensic) chemistry, classical criminalistics (shoe prints, glass fragments..), other areas such as ecology etc.
- display good understanding of major principles for formulation of hypothesis and likelihood ratios and the construction of Bayesian networks
- estimate likelihood ratios from empirical data and interpret their values on ordinal scales of conclusion
- use standard software for Bayesian networks


Basic knowledge about probability theory corresponding with an introductory course on statistics or mathematical statistics.


The course is given as a semi-distance course according to the following template: 4 weeks of concentrated lectures, seminars and computer exercises and practicals (3-4 days per week), interspaced with 3 weeks of individual studies.


The course content comprises:
- the concept of probability and Bayes’ theorem on odds form
- Bayesian hypothesis testing, hierarchy of propositions and likelihood ratios
- graphical models for probabilistic reasoning and Bayesian networks
- different types of evidence: DNA, illicit drugs and trace evidence
- transfer evidence, combination of evidence and pre-assessment
- sources of errors, fallacies and forensic interpretation on ordinal scales
- training in a computer package for Bayesian networks
- practical examples/applications from the course delegates


Taroni, F., Aitken, C, Garbolino, P. & Biedermann, A. Bayesian Networks and Probabilistic Inference in Forensic Science. Chichester: Wiley, 2006. ISBN 0470091738


Anders Nordgaard, Linköping university and Swedish National Laboratory of Forensic Sciences
Petter Mostad, Chalmers university


Anders Nordgaard


To pass, each student should show that he/she is able to apply the contents of the course in an individual project with a subject chosen from his/her own area of expertise. The project work should be presented as a written report and orally at a final seminar.




The maximum number of participants is 30.

The course is given in cooperation with the Center for Forensic Science (the National Laboratory of Forensic Science - SKL , the National Board of Forensic Medicine - RMV) and Linköping University (LiTH and HU).

Page responsible: Director of Graduate Studies