Forensic statistics encompasses all applications of statistical methodology to forensic questions (i.e., legal questions answered with scientific methods). In this project, the focus is on the use of Bayesian statistics, which is increasingly common as a framework for forensic statistics. Some examples of application areas considered:
Medical age assessment. Medical/biological observations performed on individuals may contain information about their chronological age. We study the optimal ways to make decisions about chronological age based on such age indicators. We also study consequences of some current methods for making decisions about age based on for example teeth and knee maturity.
Using DNA data for forensic identification and familial inference. We consider methods for optimizing the forensic identification process in cases of low-template DNA traces, and also in cases of large-scale database searches.
Bayesian computations for Postmortem intervals. We look at both how new types of observations can be used for improved prediction of PMIs, and how a Bayesian framework can improve communication of and decision making with PMIs.
In all projects, we employ a perspective based on Bayesian decision theory.
Professor at Chalmers, Mathematical Sciences, Applied Mathematics and Statistics
Funding Chalmers participation during 2017–
Areas of Advance