Asymptotically Optimal Sequential Testing with Markovian Data

ArXi:2602.17587v2 Announce Type: replace-cross We study one-sided and $\alpha$-correct sequential hypothesis testing for data generated by an ergodic, finite-state Marko chain. The null hypothesis is that the unknown transition matrix belongs to a prescribed set $P$ of stochastic matrices, and the alternative corresponds to a disjoint set $Q$. We establish a non-asymptotic instance-dependent lower bound on the expected stopping time of any valid sequential test under the alternative, which is asymptotically tight.