**Prerequisites**

- Basic course in (discrete and continuous) probability theory, including at least the following topics: events, random variables, conditional probability, independence. For instance, a course on the level of Sheldon Ross: A first course in probability"
- It may help if the student is familiar with the very basics of statistics, in particular basic estimation theory and hypotheses testing, but this is not strictly necessary.
- No knowledge in biology, philosophy or forensic science is assumed since we will introduce the necessary background during the lectures.

**Aim of the course**

The aim of this course is to understand the basics of the theory and philosophy of forensic statistics and probability, and to apply these to concrete situations. The course is a natural mixture of rigorous

probabilistic and statistical theory on the one hand, and elements from biology, forensic science, legal science, and philosophy on the other. We plan to discuss the following topics:

- The Bayesian framework with prior odds, posterior odds, and likelihood ratios. Likelihood ratios as measure of the strength of evidence.
- The probability and statistics of DNA evidence for trace-person matches, family relatedness, and database searches.
- Understanding likelihood ratios and likelihood ratio distributions when likelihood ratios are interpreted as random variables.
- The role of p-values in reporting forensic evidence. In particular we address the issue that p-values do not measure the strength of evidence and should not be used for that purpose.
- The classical island problems: assigning evidential weight to a shared characteristic in a closed population. We distinguish between the cold case, when an individual is randomly selected, and the search case in which the first matching individual is considered. The difference is relevant for the evidential value of a match. Also, subpopulations are discussed.
- The philosophy of probability in a forensic context. The way we interpret probability is crucial for the applicability. We will argue that only an epistemic and subjective interpretation is tenable, and discuss consequences.
- Bayesian networks - theory and applications.
- Statistical and probabilistic aspects of database searches. Databases can lead to a direct match or may point at a relative of the donor of a DNA profile.
- The uncertainty of likelihood ratios and how to deal with this.

**Rules about Homework/Exam**

There will be a written exam which counts for 70% of your final grade.

In addition, there are homework assignments. Marjan Sjerps will post one larger assignment about Bayesian Networks which counts for 10%. Ronald Meester and Klaas Slooten will both assign two smaller homework assignments, which count for 5% each.

In order to pass the course, you need to score at least a 5.0 for the exam. Any grade lower than 5.0 cannot be compensated by the homework assignments.

In case you need to do the resit, the rules remain the same. The homework assignments still count for 30%, and you need to score at least 5.0 for the resit to pass.

**Lecture Notes/Literature**

We use the book Probability and Forensic Evidence - Theory, Philosophy, and Applications, by Ronald Meester and Klaas Slooten (Cambridge University Press 2021, 454 pages). We will post a

complete pdf on the website.

**Lecturers**

Ronald Meester (VU 6 weeks), Klaas Slooten (NFI/VU 6 weeks) & Marjan Sjerps (NFI/UvA 3 weeks)

- Docent: Aafko Boonstra
- Docent: Ronald Meester
- Docent: Marjan Sjerps
- Docent: Klaas Slooten