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Hidden Markov Models with mixtures as emission distributions
2013
Statistics and computing
In unsupervised classification, Hidden Markov Models (HMM) are used to account for a neighborhood structure between observations. The emission distributions are often supposed to belong to some parametric family. In this paper, a semiparametric modeling where the emission distributions are a mixture of parametric distributions is proposed to get a higher flexibility. We show that the classical EM algorithm can be adapted to infer the model parameters. For the initialisation step, starting from
doi:10.1007/s11222-013-9383-7
fatcat:3xp2q3ymzjgchbyyshifi6tyvm