  Logical hidden Markov models (LOHMMs) upgrade traditional hidden Markov
models to deal with sequences of structured symbols in the form of logical
atoms, rather than flat characters.
  This note formally introduces LOHMMs and presents solutions to the three
central inference problems for LOHMMs: evaluation, most likely hidden state
sequence and parameter estimation. The resulting representation and algorithms
are experimentally evaluated on problems from the domain of bioinformatics.
