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. © Springer-Verlag Berlin Heidelberg 2010.
CITATION STYLE
Nánási, M., Vinař, T., & Brejová, B. (2010). The highest expected reward decoding for HMMs with application to recombination detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6129 LNCS, pp. 164–176). https://doi.org/10.1007/978-3-642-13509-5_16
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