1. Canonne, C.L., et al.: Topics and techniques in distribution testing: A biased but representative sample. Found. Trends Commun. Inf. Theor. 19(6), 1032–1198 (2022)
2. Chan, S.O., Ding, Q., Li, S.H.: Learning and testing irreducible Markov chains via the $$k$$-cover time. In: Algorithmic Learning Theory. pp. 458–480. PMLR (2021)
3. Cherapanamjeri, Y., Bartlett, P.L.: Testing symmetric Markov chains without hitting. In: Proceedings of the Thirty-Second Conference on Learning Theory. Proceedings of Machine Learning Research, vol. 99, pp. 758–785. PMLR (2019)
4. Daskalakis, C., Dikkala, N., Gravin, N.: Testing symmetric Markov chains from a single trajectory. In: Conference On Learning Theory. pp. 385–409. PMLR (2018)
5. Diakonikolas, I., Kane, D.M.: A new approach for testing properties of discrete distributions. In: 2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS). pp. 685–694. IEEE (2016)