On Computing the Total Variation Distance of Hidden Markov Models
Formal Languages and Automata Theory
2018-04-18 v1 Logic in Computer Science
Abstract
We prove results on the decidability and complexity of computing the total variation distance (equivalently, the -distance) of hidden Markov models (equivalently, labelled Markov chains). This distance measures the difference between the distributions on words that two hidden Markov models induce. The main results are: (1) it is undecidable whether the distance is greater than a given threshold; (2) approximation is #P-hard and in PSPACE.
Cite
@article{arxiv.1804.06170,
title = {On Computing the Total Variation Distance of Hidden Markov Models},
author = {Stefan Kiefer},
journal= {arXiv preprint arXiv:1804.06170},
year = {2018}
}
Comments
Technical report for an ICALP'18 paper