# Identifying combinatorially symmetric Hidden Markov Models

@article{Burgarth2017IdentifyingCS, title={Identifying combinatorially symmetric Hidden Markov Models}, author={Daniel Burgarth}, journal={arXiv: Combinatorics}, year={2017} }

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.

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