By Topic

Estimation of the accuracy of mean and variance of correlated data

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
P. M. T. Broersen ; Dept. of Appl. Phys., Delft Univ. of Technol., Netherlands

Monte Carlo simulations are an important tool in computational physics or statistical mechanics. Physical constants or properties are found as the mean or the variance of successive states of simulated systems. A new method to determine the statistical accuracy of the estimated means and variances is described. It uses the parameters of an automatically selected time series model. That time series model gives an optimal description of the spectral density and of the correlation structure of correlated data which are considered as stationary or in equilibrium. The resulting accuracy estimates are close to the Cramer-Rao bound for data where the correlation is determined by a single time constant

Published in:

IEEE Transactions on Instrumentation and Measurement  (Volume:47 ,  Issue: 5 )