Hidden Markov Models and Bayesian Networks for Counter-Terrorism
نویسندگان
چکیده
Introduction Information concerning the potential whereabouts and activities of terrorists can be extrapolated from the massive amount of intelligence reported daily to US intelligence agencies. The events encapsulated in these reports, and, in particular, the sequence of these events, can be used to probabilistically infer the activities of terrorists. In this chapter, we are particularly interested in those events which pose a threat to our national security. Our purpose is, therefore, to detect and track likely instances of terrorist activities by analyzing the sources of data, which contain partial and/or imperfect information about the identities of actors involved and their actions. Throughout the chapter, we will refer to these types of data as “transactions.” The most evident example – and likely the greatest source of information – is financial. Say, for example:
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