Abstract
Hidden Markov models (HMMs) have been applied to many real-world applications. Very often HMMs only deal with the first order transition probability distribution among the hidden states. In this paper we develop higher-order HMMs. We study the evaluation of the probability of a sequence of observations based on higher-order HMMs and determination of a best sequence of model states.
supported in part by RGC Grant No. RGC Grant No. HKU 7126/02P, and HKU CRCG Grant Nos. 10203408, 10203501, 10203907, 10203919, 10204436.
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Ching, W.K., Fung, E.S., Ng, M.K. (2003). Higher-Order Hidden Markov Models with Applications to DNA Sequences. In: Liu, J., Cheung, Ym., Yin, H. (eds) Intelligent Data Engineering and Automated Learning. IDEAL 2003. Lecture Notes in Computer Science, vol 2690. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45080-1_73
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DOI: https://doi.org/10.1007/978-3-540-45080-1_73
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