Radak Brian K
Theoretical and Computational Biophysics Group, NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801-2325, USA.
J Chem Phys. 2019 Jul 21;151(3):034105. doi: 10.1063/1.5097384.
In practical free energy estimation, the bias is often neglected once it has been shown to vanish in the large-sample limit. Yet finite-sample bias always exists and ought to be considered in any rigorous study. This work develops a metric for bias in a broad class of free energy "bridge estimators" (e.g., Bennett's method). The framework complements existing variance estimation methods and provides a means for comparing systematic and statistical errors. Examples show that, contrary to what is often assumed, the bias can be quite substantial when the sample size is modest.
在实际的自由能估计中，一旦偏差在大样本极限下被证明会消失，往往就会被忽略。然而，有限样本偏差总是存在的，并且在任何严谨的研究中都应该予以考虑。这项工作针对一类广泛的自由能“桥估计器”（例如，贝内特方法）开发了一种偏差度量。该框架补充了现有的方差估计方法，并提供了一种比较系统误差和统计误差的方法。实例表明，与通常的假设相反，当样本量适中时，偏差可能相当大。
