  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
\citep{Kall2005,Brejova2007,Gross2007,Brown2010}.
  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.
