Identifying combinatorially symmetric Hidden Markov Models
Combinatorics
2018-09-05 v1 Statistics Theory
Statistics Theory
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.
Cite
@article{arxiv.1709.02932,
title = {Identifying combinatorially symmetric Hidden Markov Models},
author = {Daniel Klaus Burgarth},
journal= {arXiv preprint arXiv:1709.02932},
year = {2018}
}
Comments
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