Alhad Sethi, Kavali Sofia Sagar, Shubhada Agrawal, Debabrota Basu, P. N. Karthik
The paper establishes an optimal sequential hypothesis testing framework for data from ergodic Markov chains with improved lower bounds on expected stopping times.
This research focuses on improving how we test hypotheses when data is generated by Markov chains, which are mathematical systems that undergo transitions from one state to another. The authors developed a new method that provides more accurate predictions of when a test should stop, compared to existing approaches. This is particularly useful for tasks like detecting errors in Markov Chain Monte Carlo simulations or checking if certain properties hold in decision-making processes modeled by Markov chains. The new method is shown to be optimal as it matches theoretical limits on performance.
