Learning and Testing Irreducible Markov Chains via the $k$-Cover Time
Proceedings of the 32nd International Conference on Algorithmic Learning Theory, PMLR 132:458-480, 2021.
We give a unified way of testing and learning finite Markov chains from a single Markovian trajectory, using the idea of $k$-cover time introduced here. The $k$-cover time is the expected length of a random walk to cover every state at least $k$ times. This generalizes the notion of cover time in the literature. The error metric in the testing and learning problems is the infinity matrix norm between the transition matrices, as considered by Wolfer and Kontorovich. Specifically, we show that if we can learn or test discrete distributions using $k$ samples, then we can learn or test Markov chains using a number of samples equal to the $k$-cover time of the chain, up to constant factors. We then derive asymptotic bounds on the $k$-cover time in terms of the number of states, minimum stationary probability and the cover time of the chain. Our bounds are tight for reversible Markov chains and almost tight (up to logarithmic factors) for irreducible ones. Our results on $k$-cover time yield sample complexity bounds for a wider range of learning and testing tasks (including learning, uniformity testing, identity testing, closeness testing and their tolerant versions) over Markov chains, and can be applied to a broader family of Markov chains (irreducible and reversible ones) than previous results which only applies to ergodic ones.