Abstract
Hidden Markov models (HMMs) have been applied to many real-world applications. Usually HMMs only deal with the first-order transition probability distribution among the hidden states, see for instance Sect.1.4. Moreover, the observable states are affected by the hidden states but not vice versa. In this chapter, we study both higher-order hidden Markov models and interactive HMMs in which the hidden states are directly affected by the observed states. We will also develop estimation methods for the model parameters in both cases.
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Ching, WK., Huang, X., Ng, M.K., Siu, TK. (2013). Hidden Markov Chains. In: Markov Chains. International Series in Operations Research & Management Science, vol 189. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-6312-2_8
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DOI: https://doi.org/10.1007/978-1-4614-6312-2_8
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