A Note on Hidden Markov Models with Application to Criminal Intelligence

Abstract

Hidden Markov Models (HMMs) which fall under the class of latent variable models have received widespread attention in many fields of applications. HMMs were initially developed and applied within the context of speech recognition. The theoretical framework underpinning the formalism of HMMs has also evolved over time and has found an exalted place in the theory of stochastic processes.  The three problems HMMs are used to resolve were discussed alongside their solutions in this paper. An application to criminal intelligence in unraveling the culprit in a situation involving theft was also carried out and results obtained indicated that the HMMs approach offered a similar result with that of the well-established Dynamic Programming approach.