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Monte Carlo approaches to hidden Markov model state estimation


In this paper, we develop a Monte Carlo approach for hidden Markov model (HMM) order estimation-finding the underlying number of states in a hidden Markov model. We compare predictions and true observations using classification rates, correlations, and ROC curves as statistical estimators. Tests are run on both artificail data in a controlled experiment and on real-world data sets--Abstract.

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