Can I read Making the most of data: Quantum Monte Carlo Post-Analysis Revisited on EtoBox?

Making the most of data: Quantum Monte Carlo Post-Analysis Revisited by Ichibha, Tom; Hongo, Kenta; Maezono, Ryo; Thom, Alex J. W. is a scholarly article available to read on EtoBox.

What is Making the most of data: Quantum Monte Carlo Post-Analysis Revisited about?

In quantum Monte Carlo (QMC) methods, energy estimators are calculated as the statistical average of the Markov chain sampling of energy estimator along with an associated statistical error. This error estimation is not straightforward and there are several choices of the error estimation methods. We evaluate the performance of three methods, Straatsma, an autoregressive model, and a blocking analysis based on von Neumann's ratio test for randomness, for the energy time-series given by Diffusion Monte Carlo, Full Configuration Interaction Quantum Monte Carlo and Coupled Cluster Monte Carlo methods. From these analyses we describe a hybrid analysis method which provides reliable error estimates for series of all lengths. Equally important is the estimation of the appropriate start point of the equilibrated phase, and two heuristic schemes are tested, establishing that MSER (mean squared error rule) gives reasonable and constant estimations independent of the length of time-series.

Author

Ichibha, Tom; Hongo, Kenta; Maezono, Ryo; Thom, Alex J. W.

Published

2019

Language

EN