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dc.contributor.authorLi, Jun
dc.contributor.authorVignal, Philippe
dc.contributor.authorSun, Shuyu
dc.contributor.authorCalo, Victor M.
dc.date.accessioned2015-08-03T11:45:50Z
dc.date.available2015-08-03T11:45:50Z
dc.date.issued2015-06-03
dc.identifier.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
dc.identifier.issn18152406
dc.identifier.doi10.4208/cicp.110613.280214a
dc.identifier.urihttp://hdl.handle.net/10754/563327
dc.description.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.
dc.description.sponsorshipThis 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".
dc.publisherGlobal Science Press
dc.subjectBlocking method
dc.subjectGibbs ensemble
dc.subjectMarkov Chain Monte Carlo method
dc.subjectMolecular simulation
dc.subjectPhase coexistence
dc.subjectVariance estimation
dc.titleOn stochastic error and computational efficiency of the Markov Chain Monte Carlo method
dc.typeArticle
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputational Transport Phenomena Lab
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentEnvironmental Science and Engineering Program
dc.contributor.departmentMaterial Science and Engineering Program
dc.contributor.departmentNumerical Porous Media SRI Center (NumPor)
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalCommunications in Computational Physics
dc.identifier.arxivid1204.3176
kaust.personVignal, Philippe
kaust.personSun, Shuyu
kaust.personCalo, Victor M.
kaust.personLi, Jun
kaust.acknowledged.supportUnitCenter for Numerical Porous Media
dc.date.published-online2015-06-03
dc.date.published-print2014-08


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