Minimax Testing of Identity to a Reference Ergodic Markov Chain
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:191-201, 2020.
We exhibit an efficient procedure for testing, based on a single long state sequence, whether an unknown Markov chain is identical to or e-far from a given reference chain. We obtain nearly matching (up to logarithmic factors) upper and lower sample complexity bounds for our notion of distance, which is based on total variation. Perhaps surprisingly, we discover that the sample complexity depends solely on the properties of the known reference chain and does not involve the unknown chain at all, which is not even assumed to be ergodic.