Hidden Markov Chains

Wai-Ki Ching, Ximin Huang, Michael K. Ng and Tak Kuen Siu
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Wai-Ki Ching: The University of Hong Kong
Ximin Huang: Georgia Institute of Technology
Michael K. Ng: Hong Kong Baptist University

Chapter Chapter 8 in Markov Chains, 2013, pp 201-230 from Springer

Abstract: 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.

Keywords: Observable State; Hide State; Transition Probability Matrix; Default Probability; Observation Sequence (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-1-4614-6312-2_8

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DOI: 10.1007/978-1-4614-6312-2_8

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