Effectiveness of Applying a Bayesian Network to the Offender Profiling: Stochastic Inference of the Indoor-sex-offender’s Employment

Abstract

OŠender proˆling is one of the tools of decision making for criminal investigation. It is a set of techniques to infer characteristics of an unknown oŠender, such as sex, age bracket, lifestyle, psychological feature, previous crime, inhabited area, from the information which is left at the crime scene. In this article, we proposed a tool of decision-making for criminal investigation from the perspective of prediction of an uncertain event by the use of a Bayesian Network (BN). BN is a probability model that describes causal structure of events as chain networks of conditional probability, and is capable to predict the possibility of uncertain events. To examine the validity of the constructed model, ˆrstly, we divided previous oŠenders' information of the indoor-sex-oŠence cases into a training data (9,859 cases) and validation data (50 cases). Secondly, we constructed a model from the training data by means of K2 and MDL (minimum description length) as search-algorithm and information criteria, respectively. Finally, the validity of the model was examined by the validation data as virtual cases.