S. Motoi, Y. Nakada, T. Misu, T. Matsumoto, and N. Yagi (Japan)
Hidden Markov Model, Bayesian learning, Markov Chain Monte Carlo, Redundant Component, Hyperparameter
This paper proposes a novel Bayesian Hidden Markov Model for multi-dimensional discrete time-series data. The proposed model has hyperparameters, which correspond to the dependencies of the data components on the hidden states. By adjusting these hyperparameters, the proposed model enables a reduction in negative influences from in effective data components. This paper also describes an implementation method for the proposed model using the Markov Chain Monte Carlo method. The performance of the proposed model is evaluated via two examples.
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