Error bars for Markov chain Monte Carlo data streams

Error bars are typically assigned to Markov chain Monte Carlo data by either an uncorrelated analysis of block-averaged data or a truncated summation of the autocorrelation function. These analysis methods depend on a choice of either a block size or a truncation point in addition to a choice of equilibration point separating equilibrated from unequilibrated data. In this talk, we present a hierarchical analysis method combining block averaging and autocorrelation summation that efficiently determines the equilibration and truncation points to a predetermined relative precision. Furthermore, we implement this method to accommodate the input of arbitrarily partitioned data streams and the output of error bars on demand.