  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.
