On stochastic error and computational efficiency of the Markov Chain Monte Carlo method
KAUST DepartmentApplied Mathematics and Computational Science Program
Computational Transport Phenomena Lab
Earth Science and Engineering Program
Environmental Science and Engineering Program
Material Science and Engineering Program
Numerical Porous Media SRI Center (NumPor)
Physical Science and Engineering (PSE) Division
Online Publication Date2015-06-03
Print Publication Date2014-08
Permanent link to this recordhttp://hdl.handle.net/10754/563327
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AbstractIn Markov Chain Monte Carlo (MCMC) simulations, thermal equilibria quantities are estimated by ensemble average over a sample set containing a large number of correlated samples. These samples are selected in accordance with the probability distribution function, known from the partition function of equilibrium state. As the stochastic error of the simulation results is significant, it is desirable to understand the variance of the estimation by ensemble average, which depends on the sample size (i.e., the total number of samples in the set) and the sampling interval (i.e., cycle number between two consecutive samples). Although large sample sizes reduce the variance, they increase the computational cost of the simulation. For a given CPU time, the sample size can be reduced greatly by increasing the sampling interval, while having the corresponding increase in variance be negligible if the original sampling interval is very small. In this work, we report a few general rules that relate the variance with the sample size and the sampling interval. These results are observed and confirmed numerically. These variance rules are derived for theMCMCmethod but are also valid for the correlated samples obtained using other Monte Carlo methods. The main contribution of this work includes the theoretical proof of these numerical observations and the set of assumptions that lead to them. © 2014 Global-Science Press.
CitationLi, J., Vignal, P., Sun, S., & Calo, V. M. (2014). On Stochastic Error and Computational Efficiency of the Markov Chain Monte Carlo Method. Communications in Computational Physics, 16(2), 467–490. doi:10.4208/cicp.110613.280214a
SponsorsThis work was supported in part by the King Abdullah University of Science and Technology (KAUST) Center for Numerical Porous Media. In addition, S. Sun would also like to acknowledge the support of this study by a research award from King Abdulaziz City for Science and Technology (KACST) through a project entitled "Study of Sulfur Solubility using Thermodynamics Model and Quantum Chemistry".
PublisherGlobal Science Press