A Note on the Mixture Transition Distribution and Hidden Markov Models
نویسنده
چکیده
We discuss an interpretation of the Mixture Transition Distribution (MTD) for discretevalued time series which is based on a sequence of independent latent variables which are occasion-specific. We show that, by assuming that this latent process follows a first order Markov Chain, MTD can be generalized in a sensible way. A class of models results which also includes the Hidden Markov Model (HMM). For these models we outline an EM algorithm for the maximum likelihood estimation which exploits recursions developed within the HMM literature.
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تاریخ انتشار 2008