g_wham—A Free Weighted Histogram Analysis Implementation Including Robust Error and Autocorrelation Estimates
Max Planck Institute for Biophysical Chemistry · Uppsala University
Abstract
The Weighted Histogram Analysis Method (WHAM) is a standard technique used to compute potentials of mean force (PMFs) from a set of umbrella sampling simulations. Here, we present a new WHAM implementation, termed g_wham, which is distributed freely with the GROMACS molecular simulation suite. g_wham estimates statistical errors using the technique of bootstrap analysis. Three bootstrap methods are supported: (i) bootstrapping new trajectories based on the umbrella histograms, (ii) bootstrapping of complete histograms, and (iii) Bayesian bootstrapping of complete histograms, that is, bootstrapping via the assignment of random weights to the histograms. Because methods ii and iii consider only complete…
Citation impact
- FWCI
- 11.57
- Percentile
- 100%
- References
- 20
Authors
3Topics & keywords
- Bootstrapping (finance)
- Histogram
- Autocorrelation
- Computer science
- Algorithm
- Umbrella sampling
- Sampling (signal processing)
- Bayesian probability