Abstract
Hidden Markov models are traditionally decoded by the Viterbi algorithm which finds the highest probability state path in the model. In recent years, several limitations of the Viterbi decoding have been demonstrated, and new algorithms have been developed to address them (Kall et al., 2005; Brejova et al., 2007; Gross et al., 2007; Brown and Truszkowski, 2010). In this paper, we propose a new efficient highest expected reward decoding algorithm (HERD) that allows for uncertainty in boundaries of individual sequence features. We demonstrate usefulness of our approach on jumping HMMs for recombination detection in viral genomes.
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Nánási, M., Vinař, T., Brejová, B. (2010). The Highest Expected Reward Decoding for HMMs with Application to Recombination Detection. In: Amir, A., Parida, L. (eds) Combinatorial Pattern Matching. CPM 2010. Lecture Notes in Computer Science, vol 6129. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13509-5_16
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DOI: https://doi.org/10.1007/978-3-642-13509-5_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13508-8
Online ISBN: 978-3-642-13509-5
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