@inbook{b0fb338cedb94ccdaff353ba87f03e47,
title = "Chapter 2 Quantifying Uncertainty and Sampling Quality in Biomolecular Simulations",
abstract = "Growing computing capacity and algorithmic advances have facilitated the study of increasingly large biomolecular systems at longer timescales. However, with these larger, more complex systems come questions about the quality of sampling and statistical convergence. What size systems can be sampled fully? If a system is not fully sampled, can certain {"}fast variables{"} be considered well converged? How can one determine the statistical significance of observed results? The present review describes statistical tools and the underlying physical ideas necessary to address these questions. Basic definitions and ready-to-use analyses are provided, along with explicit recommendations. Such statistical analyses are of paramount importance in establishing the reliability of simulation data in any given study.",
keywords = "block averaging, convergence, correlation time, equilibrium ensemble, ergodicity, error analysis, principal component, sampling quality",
author = "Alan Grossfield and Zuckerman, {Daniel M.}",
note = "Funding Information: D.M. Zuckerman would like to acknowledge in-depth conversations with Edward Lyman and Xin Zhang, as well as their assistance in preparing the figures. Insightful discussions were also held with Divesh Bhatt, Ying Ding, Artem Mamonov, and Bin Zhang. Support for DMZ was provided by the NIH (Grants GM076569 and GM070987) and the NSF (Grant MCB-0643456). AG would like to thank Tod Romo for helpful conversations and assistance in figure preparation. ",
year = "2009",
doi = "10.1016/S1574-1400(09)00502-7",
language = "English (US)",
isbn = "9780444533593",
series = "Annual Reports in Computational Chemistry",
pages = "23--48",
editor = "Ralph Wheeler",
booktitle = "Annual Reports in Computational Chemistry",
}