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Hidden Markov models are traditionally decoded by the Viterbi algorithm whichfinds the highest probability state path in the model. In recent years, severallimitations of the Viterbi decoding have been demonstrated, and new algorithmshave been developed to address them\citep{Kall2005,Brejova2007,Gross2007,Brown2010}.  In this paper, we propose a new efficient highest expected reward decodingalgorithm (HERD) that allows for uncertainty in boundaries of individualsequence features. We demonstrate usefulness of our approach on jumping HMMsfor recombination detection in viral genomes.

The Highest Expected Reward Decoding for HMMs with Application to Recombination Detection