arXiv:2602.17587v2 Announce Type: replace-cross Abstract: We study one-sided and $\alpha$-correct sequential hypothesis testing for data generated by an ergodic, finite-state Markov 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. Our novel analysis improves the existing lower bounds, which are eit
This is a new publication on arXiv, indicating ongoing academic research in the field of statistical learning.
For a strategic reader, this specific research is highly theoretical and contributes to foundational understanding rather than immediate practical application.
This paper refines theoretical bounds for sequential hypothesis testing; it does not change current practices or technologies.
This research provides improved analytical tools for certain types of sequential hypothesis testing problems.
Over time, refined theoretical understanding can underpin more robust and efficient AI algorithms in specific applications but not broadly.
These foundational mathematical advances might eventually contribute to the development of more sophisticated AI agent decision-making processes under uncertainty.
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