Modeling of Human Criminal Behavior using Probabilistic Networks

TL;DR

This paper introduces a probabilistic network approach to systematically analyze crime scene data, enabling more accurate offender profiling and insight into behavioral patterns for criminal investigations.

Contribution

It presents a novel application of probabilistic networks for crime scene analysis, improving the systematic discovery of behavioral patterns and offender profiling.

Findings

Probabilistic networks effectively model crime scene variables.

The model can infer offender characteristics from crime scene data.

Enhanced decision support for criminal profiling.

Abstract

Currently, criminals profile (CP) is obtained from investigators or forensic psychologists interpretation, linking crime scene characteristics and an offenders behavior to his or her characteristics and psychological profile. This paper seeks an efficient and systematic discovery of nonobvious and valuable patterns between variables from a large database of solved cases via a probabilistic network (PN) modeling approach. The PN structure can be used to extract behavioral patterns and to gain insight into what factors influence these behaviors. Thus, when a new case is being investigated and the profile variables are unknown because the offender has yet to be identified, the observed crime scene variables are used to infer the unknown variables based on their connections in the structure and the corresponding numerical (probabilistic) weights. The objective is to produce a more systematic and empirical approach to profiling, and to use the resulting PN model as a decision tool.