In this talk, I show how Bayesian networks can be used for missing person identification in forensics. In large scale disasters, it is tremendously important that victims of tragedies are quickly and accurately identified. But when there may be limited information available, and only partial DNA data, the task of identification can become very difficult and time consuming. For this purpose, we developed the Bonaparte Disaster Victim Identification system together with the Netherlands Forensic Institute. Bonaparte uses Bayesian networks to model statistical relationships of genetic material of relatives and calculates statistical likelihoods of the missing people being a member of each family. In this way data from many members of the same family can be used in combination to identify victims. Bonaparte DVI was first used by the Netherland Forensic Institute to match 129 body parts from a plane crash in Libya in 2010. More recently Bonaparte was used in the identification of the perpetrator in a 13-year-old murder case.