Title: Identifying combinatorially symmetric Hidden Markov Models,
Abstract: We provide a sufficient criterion for the unique parameter identification of
combinatorially symmetric Hidden Markov Models based on the structure of their
transition matrix. If the observed states of the chain form a zero forcing set
of the graph of the Markov model then it is uniquely identifiable and an
explicit reconstruction method is given.