@Article{CiCP-15-1352, 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 = {

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.

}, issn = {1991-7120}, doi = {https://doi.org/10.4208/cicp.190313.171013a}, url = {http://global-sci.org/intro/article_detail/cicp/7141.html} }