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732A45 Statistical Evidence Evaluation

Course information


This course is about evidence interpretation and evaluation. Evidence is a broad term that applies to several forms of information used in the society. The closest association with this word is however forensic science. Despite this clear association, a lot of the models and the principles of reasoning in forensic science can be generalised and used in other applications, for instance evidence based medicine. The generalisation is in principle about making decisions under uncertainty. Decision theory is sometimes considered to be an advanced merely theoretic construction within the statistical science, but lessons learnt from its introduction in the forensic field shows that on the contrary the theoretical framework may be both intuitive and fairly easy to approach. In this course we will therefore build the knowledge upon litterature and developed practices within forensic science, but still emphasise the generality of the models and methods.

Some words about forensic science

The word "forensic" stems from the latin word "forum" that stands for square, i.e. an open place in a town. In the ancient Roman empire justice was administered at the main square of a town (cf. "Forum Romanum" in Rome, Italy), and part of this process was to discuss what evidence there was that either supported the prosecutor's case of the defendant's case.Forensic science stands for a conglomerate of different scientific disciplines gathered together to secure, analyse and iterpret (technical) evidence to be used in the legal process of particular cases. It does not always have to be related to a crime. For instance, finding the identity of a dead person that is a victim of a disaster is a legal process, since it has to be legally stated who that person is (due to various consequences of their death). Another non-criminal forensic case may be to investigate if an individual should be entitled to keep his driving licence in spite of having contracted a serious disease. Most forensic casework is however related to a committed crime and comprises a vast variety of analysis methods ranging from professional visual inspection to chemical analyses with a nuclear magnetic resonance camera.

Forensic evidence evaluation is the part of forensic science that deals with interpretation of the findings. Assume for instance that a shoemark has been secured at a crime scene and that it has been compared with a shoe owned by a person suspected to be the perpetrator. Such a comparison may reveal a lot of similarities (matching sole patterns, matching in size, matching in some details due to wearing of the shoe, etc.). It may be assumed that if enough similarities are found the case should be stated as clear, but that is very seldom the situation. On the contrary, found similarities may not at all be extremely rare and it is very important to sort out the rarities. Moreover, should we yet come to the conclusion that the similarities are almost uniqe (they cannot possibly be absolutely uniqe since it is practically impossible to overview all shoes in the entire world) we still haven't proved that the shoe actually did leave the mark. What if the shoe was found in Kuala Lumpur and the mark was found in New Orleans, and there was hardly time to transport the shoe to Kuala Lumpur from the time-point the mark was made until the time-point when the shoe was discovered?

This course

In the course we will introduce and explore statistical models for evaluating the strength of the findings (mainly forensic findings) with respect to propositions (hypothesis) that can be proposed as explanations to the evidence material obtained. Essential for this evaluation is the concept of Bayesian hypothesis testing, which will play a key role throughout the course. Bayesian hypothesis testing is a very general concept for handling uncertainty in observational data with respect to different explanations to why they occured. In the course we will show the generality of the methods and take up their conjuction with decision making. Much of this work can be done in terms of graphical modelling, which is a convenient tool both for developers and practitionners in pursuing statistical evaluation and interpretation.


Course content

The course content comprises:

  • Probabilistic reasoning
  • Concepts of decision theory and Bayesian inference
  • Graphical models: Bayesian belief networks (BBN) and influence diagrams
  • Sampling rules for decision making
  • Some specific types of forensic evidence
  • Evaluation of evidence in hierarchies of propositions (in forensic applications: source level, activity level, crime level)
  • Reporting evidentiary strength


Teaching

The teaching comprises lectures, tutorials and seminars. The lectures are devoted to presentations of theories, concepts, and methods. The tutorials will consist of practical exercises using a computer. The seminars comprise student presentations and discussions of assignments.


Examination

The course will be examined through

  • Assignments encompassing both theoretical and computer-based exercises
  • One final oral examination
The final examination will be oral because the concepts are such that acquired knowledge is better shown through discussions than through written argumentation around a static case. The possibility to put follow-up questions to answers is essential for the examination.


Course literature

There is a variety of books that can be used to cover major parts of the contents of the course, but there is no particular text that covers it all. For this purpose a main book has been chosen, which will be supplemented by scientific papers. The main textbook will be

  • Taroni F., Bozza S., Garbolino P., Biedermann A., Aitken C. (2010). Data Analysis in Forensic Science: A Bayesian Decision Perspective. Chichester: Wiley-Blackwell. ISBN 9780470998359
  • A supplementary text that will be used is
    • Gittelson S. (2013). Evolving from Inferences to Decisions in the Interpretation of Scientific Evidence. Thčse de Doctorat, Série criminalistique LVI, Université de Lausanne. ISBN 2-940098-60-3. Available at http://www.unil.ch/webdav/site/esc/shared/These_Gittelson.pdf.
    A supplementary textbook is
    • Taroni F., Biedermann A., Bozza S., Garbolino P., Aitken C. (2006). Bayesian Networks and Probabilistic Inference and Decision Analysis in Forensic Science. 2nd ed. Chichester: John Wiley & Sons Ltd. ISBN 9780470979730


Course responsible and tutor

Anders Nordgaard. E-mail: Anders.Nordgaard@liu.se. Phone: +46 10 562 8013.


Course start

Thursday 27 August at 13.15 in room John von Neumann


Page responsible: Anders Nordgaard
Last updated: 2015-08-12