where N b = N n 0 /b is the number of blocks, assuming b is a factor of N n 0 . This relies on the fact that the batch means become uncorrelated as b → ∞. This method implicitly accounts for autocorrelation in the MSER-m family of methods, where the data are blockaveraged using blocks of size m before MSER is applied. However, using overlapping batches, rather than the non-overlapping batches in Equation , yields a less noisy variance estimate. The overlapping batch means variance estimate is effectively equivalent to a lag window estimate (Equation ) using a Bartlett lag window (a kind of triangular window) of the appropriate size, and therefore Equation 20 can be thought of as a noisy (if cheaper) special case of Equation . Hence, we do not consider the batch means method. The circular bootstrap and moving blocks bootstrap methods are also asymptotically equivalent to the overlapping batch means method. A final class of methods terminates the sum of autocovariance terms according to Markov chain-specific criteria. These are Geyer's initial sequence estimators. Geyer noted that for stationary, reversible, and irreducible Markov chains, the sequence of sums of pairs of covariance terms