Alternating Markov Chains for Distribution Estimation in the Presence of Errors
Information Theory
2012-02-07 v1 math.IT
Abstract
We consider a class of small-sample distribution estimators over noisy channels. Our estimators are designed for repetition channels, and rely on properties of the runs of the observed sequences. These runs are modeled via a special type of Markov chains, termed alternating Markov chains. We show that alternating chains have redundancy that scales sub-linearly with the lengths of the sequences, and describe how to use a distribution estimator for alternating chains for the purpose of distribution estimation over repetition channels.
Cite
@article{arxiv.1202.0925,
title = {Alternating Markov Chains for Distribution Estimation in the Presence of Errors},
author = {Farzad Farnoud and Narayana P. Santhanam and Olgica Milenkovic},
journal= {arXiv preprint arXiv:1202.0925},
year = {2012}
}