The Dynamic Exponent of the Two-Dimensional Ising Model and Monte Carlo   Computation of the Sub-Dominant Eigenvalue of the Stochastic Matrix
  We introduce a novel variance-reducing Monte Carlo algorithm for accurate determination of autocorrelation times. We apply this method to two-dimensional Ising systems with sizes up to $15 \times 15$, using single-spin flip dynamics, random site selection and transition probabilities according to the heat-bath method. From a finite-size scaling analysis of these autocorrelation times, the dynamical critical exponent $z$ is determined as $z=2.1665$ (12). 
M. P. Nightingale (Department of Physics, University of Rhode Island,   Kingston, RI) and H.W.J. Bl\"ote (Department of Appl...