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Testing from One Sample: Is the casino really using a riffle shuffle?

Abstract · Apr 22, 2017 21:02 ·

eecs testing supported award nsf markov chains mit cs-lg cs-ds

Arxiv Abstract

  • Constantinos Daskalakis
  • Nishanth Dikkala
  • Nick Gravin

Classical distribution testing assumes access to i.i.d. samples from the distributions that are being tested. We initiate the study of Markov chain testing, assuming access to a single sample from the Markov Chains that are being tested. In particular, we get to observe a single trajectory X_0 ,…,X_t ,… of an unknown Markov Chain M, for which we do not even get to control the distribution of the starting state X_0 . Our goal is to test whether M is identical to a model Markov Chain M_0 . In the first part of the paper, we propose a measure of difference between two Markov chains, which captures the scaling behavior of the total variation distance between words sampled from the Markov chains as the length of these words grows. We provide efficient and sample near- optimal testers for identity testing under our proposed measure of difference. In the second part of the paper, we study Markov chains whose state space is exponential in their description, providing testers for testing identity of card shuffles. We apply our results to testing the validity of the Gilbert, Shannon, and Reeds model for the riffle shuffle.

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