@Article{CiCP-15-5,
author = {},
title = {The Lognormal Distribution and Quantum Monte Carlo Data},
journal = {Communications in Computational Physics},
year = {2014},
volume = {15},
number = {5},
pages = {1352--1367},
abstract = {<p style="text-align: justify;">Quantum Monte Carlo data are often afflicted with distributions that resemble lognormal probability distributions and consequently their statistical analysis
cannot be based on simple Gaussian assumptions. To this extent a method is introduced to estimate these distributions and thus give better estimates to errors associated with them. This method entails reconstructing the probability distribution of a
set of data, with given mean and variance, that has been assumed to be lognormal
prior to undergoing a blocking or renormalization transformation. In doing so, we
perform a numerical evaluation of the renormalized sum of lognormal random variables. This technique is applied to a simple quantum model utilizing the single-thread
Monte Carlo algorithm to estimate the ground state energy or dominant eigenvalue of
a Hamiltonian matrix.</p>},
issn = {1991-7120},
doi = {https://doi.org/10.4208/cicp.190313.171013a},
url = {https://global-sci.com/article/80538/the-lognormal-distribution-and-quantum-monte-carlo-data}
}