Paper:
Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path
This article provides the first procedure for computing a fully data-dependent interval that traps the mixing time t mix of a finite reversible ergodic Markov chain at a prescribed confidence level. The interval is computed from a single finite-length sample path from the Markov chain, and does not require the knowledge of any parameters of the chain. This stands in contrast to previous approaches, which either only provide point estimates, or require a reset mechanism, or additional prior knowledge. The interval is constructed around the relaxation time t relax , which is strongly related to the mixing time, and the width of the interval converges to zero roughly at a √ n rate, where n is the length of the sample path. Upper and lower bounds are given on the number of samples required to achieve constant-factor multiplicative accuracy. The lower bounds indicate that, unless further restrictions are placed on the chain, no procedure can achieve this accuracy level before seeing each state at least Ω(t relax ) times on the average. Finally, future directions of research are identified.

Review:
1. The paper would be much more interesting to the NIPS audience if the authors provided an application of their work to a practical machine learning example, e.g., MCMC for reinforcement learning (as stated for motivation in the Introduction) or for marginalization of a graphical model. Without such an example, which would establish tightness of the proposed bounds and confidence intervals, it is difficult to appreciate the practical utility of the theoretical results presented in the paper. 2. After Theorem 1, in the statement that each state may have to be visited \log(d)/\gamma_* times on average, "at least" should be added considering the non-uniformity of the state frequencies. 3. In Theorems 1 and 2, since a lower bound on the number of observations is sought, it is more appropriate to present the results for n \geq c/\overline{\pi} and n \geq cd log(d)/\overline{\gamma}, instead of the reversed inequalities in these theorem statements. If the authors adopted this they would obtain upper bounds instead of lower bounds, on the error probabilities shown in each theorem.4. It is not clear how (4) and (5) in Theorem 3 imply n \tilde{O}\left(\frac{1}{\pi_* \gamma_*} \right) andn \tilde{O}\left(\frac{1}{\pi_* \gamma_*^3} \right), respectively. Indeed, one can easily see (suppressing the logarithmic terms) that (4) impliesn \tilde{O}\left(\max\left\{\frac{\pi_*}{\gamma_*}, \frac{1}{\gamma_*} \right\} \right) \tilde{O}\left(\frac{1}{\gamma_*} \right)and similarly, that (5) implies n \tilde{O}\left(\max\left\{\frac{\pi_*\gamma_*}{\gamma_*}, \frac{1}{\gamma_*} \right\} \right) \tilde{O}\left(\frac{1}{\pi_*\gamma_*} \right)However, these do not imply the desired result. More explanation is needed.  5. The lower bound on the number of samples stated in Main Results is not the same as the lower bound in Theorem 2. The former lower bound is \Omega((d\log d)/\gamma_*+1/\pi_*) while the latter lower bound is missing the term 1/\pi_*. Explanation of this discrepancy is required. 6. There seems to be a problem with the proof of Theorem 1 in Appendix B. The following counterexample violates the claims in the proof. Let \overline{\pi} 1/4 for which \epsilon 2/3 and \lambda_* -1/6 < 0, which violates the definition \lambda_* \max(\lambda_2,|\lambda_d|). Furthermore, for n < c(2+\epsilon)/\epsilon 4c, choosing c 1, for which n 3, we compute the probability of staying in state 1 as (1-\epsilon)^n 1/27 which is less than the presented result of 1/4.7. The last sentence of the proof of Thm 2 requires elaboration. It is not clear how it follows. Minor comments1. In the first paragraph of the Related Work the authors need clearly state the assumptions on the Markov chain, e.g. reversible, ergodic, aperiodic, irreducible, finite-state.2. The title of Section 3 is misleading since they are not performing "point estimation" but rather interval estimation. 3. The authors repeated claims of obtaining confidence intervals that are "empirical" and "based on a single sample path" are a overemphasized. Any useful confidence interval must be empirical and by definition, a confidence interval is an interval estimate, and always depends on a given sample.