ArticleJournal of chemical theory and computation2019
Statistical Uncertainty Analysis for Small-Sample, High Log-Variance Data: Cautions for Bootstrapping and Bayesian Bootstrapping.
Article in Journal of chemical theory and computation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Single-cell morphodynamical trajectories enable prediction of gene expression accompanying cell state change.Cell systems · 2026Article
- Uncertainty Quantification forChemical reviews · 2026Review
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- Forming bootstrap confidence intervals and examining bootstrap distributions of standardized coefficients in structural equation modelling: A simplified workflow using the R package semboottools.Behavior research methods · 2026Article
- The individual-level precision of implicit measures.Behavior research methods · 2025Article
- Unsupervised Learning of Progress Coordinates during Weighted Ensemble Simulations: Application to NTL9 Protein Folding.Journal of chemical theory and computation · 2025Article
- A FAIR-Compliant Management Solution for Molecular Simulation Trajectories.Journal of chemical information and modeling · 2025Article
- Rare-Event Sampling using a Reinforcement Learning-Based Weighted Ensemble Method.bioRxiv : the preprint server for biology · 2024Article
- WEDAP: A Python Package for Streamlined Plotting of Molecular Simulation Data.Journal of chemical information and modeling · 2024Article
- Revisiting Textbook Azide-Clock Reactions: A "Propeller-Crawling" Mechanism Explains Differences in Rates.Journal of the American Chemical Society · 2024Article
- Morphodynamical cell state description via live-cell imaging trajectory embedding.Communications biology · 2023Article
- A Suite of Tutorials for the WESTPA 2.0 Rare-Events Sampling Software [Article v2.0].Living journal of computational molecular science · 2023Article
- Reproducible Inter-Personal Brain Coupling Measurements in Hyperscanning Settings With functional Near Infra-Red Spectroscopy.Neuroinformatics · 2022Article
- WESTPA 2.0: High-Performance Upgrades for Weighted Ensemble Simulations and Analysis of Longer-Timescale Applications.Journal of chemical theory and computation · 2022Article
- A gentle introduction to the non-equilibrium physics of trajectories: Theory, algorithms, and biomolecular applications.American journal of physics · 2021Article
- Mechanistic analysis of light-driven overcrowded alkene-based molecular motors by multiscale molecular simulations.Physical chemistry chemical physics : PCCP · 2021Article
- The RED scheme: Rate-constant estimation from pre-steady state weighted ensemble simulations.The Journal of chemical physics · 2021Article
- Accelerated Estimation of Long-Timescale Kinetics from Weighted Ensemble Simulation via Non-Markovian "Microbin" Analysis.Journal of chemical theory and computation · 2020Article
- Impact of Bayesian Inference on the Selection of Psidium guajava.Scientific reports · 2020Article
- Permeability of membranes in the liquid ordered and liquid disordered phases.Nature communications · 2019Article
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Authors and funding
2 authors.
Funding
Abstract
Recent advances in molecular simulations allow the evaluation of previously unattainable observables, such as rate constants for protein folding. However, these calculations are usually computationally expensive, and even significant computing resources may result in a small number of independent estimates spread over many orders of magnitude. Such small-sample, high "log-variance" data are not readily amenable to analysis using the standard uncertainty (i.e., "standard error of the mean") because unphysical negative limits of confidence intervals result. Bootstrapping, a natural alternative guaranteed to yield a confidence interval within the minimum and maximum values, also exhibits a striking systematic bias of the lower confidence limit in log space. As we show, bootstrapping artifactually assigns high probability to improbably low mean values. A second alternative, the Bayesian bootstrap strategy, does not suffer from the same deficit and is more logically consistent with the type of confidence interval desired. The Bayesian bootstrap provides uncertainty intervals that are more reliable than those from the standard bootstrap method but must be used with caution nevertheless. Neither standard nor Bayesian bootstrapping can overcome the intrinsic challenge of underestimating the mean from small-size, high log-variance samples. Our conclusions are based on extensive analysis of model distributions and reanalysis of multiple independent atomistic simulations. Although we only analyze rate constants, similar considerations will apply to related calculations, potentially including highly nonlinear averages like the Jarzynski relation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.