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.
 Publication:

arXiv eprints
 Pub Date:
 September 2017
 arXiv:
 arXiv:1709.02932
 Bibcode:
 2017arXiv170902932B
 Keywords:

 Mathematics  Combinatorics;
 Mathematics  Statistics Theory
 EPrint:
 Although the result is very simple, I could not find much (closely) related work. If I missed out something I'd be grateful if you could let me know via email to dkb3@aber.ac.uk