Diagnostic tools for evaluating and updating hidden Markov models

In this paper we consider two related problems in hidden Markov models (HMMs). One, how the various parameters of an HMM actually contribute to predictions of state sequences and spatio-temporal pattern recognition. Two, how the HMM parameters (and associated HMM topology) can be updated to improve performance. These issues are examined in the context of four different experimental settings from pure simulations to observed data. Results clearly demonstrate the benefits of applying some critical tests on the model parameters before using it as a predictor or spatio-temporal pattern recognition technique.