Alternating Markov Chains for Distribution Estimation in the Presence of Errors

Farzad Farnoud, Narayana P. Santhanam, Olgica Milenkovic

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.

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