Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Random Error01:04

Random Error

Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.
Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
Standard Error of the Mean01:13

Standard Error of the Mean

The sampling variability of a statistic is defined as how much the statistic varies from one sample to another. The sampling variability of a statistic is typically measured by measuring its standard error.The standard error of the mean is an example of a standard error. It is a unique standard deviation known as the standard deviation of the sampling distribution of the mean. The standard error of the mean is a statistic that calculates how correctly a sample distribution represents a...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Charge Dependence of Local Hydration Dynamics in Poly(Acrylic Acid) Solutions.

Langmuir : the ACS journal of surfaces and colloids·2026
Same author

Context-Aware Hydrophobicity Modeling: HydroMap and FastHydroMap.

bioRxiv : the preprint server for biology·2026
Same author

amyloid-predict and LLPS-predict: Predicting phase separation propensities in the intrinsically disordered proteome.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Prediction of cationic surfactant phase diagrams via molecularly informed field theory.

Journal of colloid and interface science·2026
Same author

BAG2 Condensates Couple Proteostasis to CD8<sup>+</sup>T Cell Surveillance.

bioRxiv : the preprint server for biology·2026
Same author

A systematic methodology to develop bottom-up coarse-grained models for sequence-specific polypeptoids.

The Journal of chemical physics·2025
Same journal

Tension on dsDNA bound to ssDNA-RecA filaments may play an important role in driving efficient and accurate homology recognition and strand exchange.

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Amplitude-phase coupling drives chimera states in globally coupled laser networks [Phys. Rev. E 91, 040901(R) (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Erratum: Shapes of sedimenting soft elastic capsules in a viscous fluid [Phys. Rev. E 92, 033003 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Erratum: Attenuation of excitation decay rate due to collective effect [Phys. Rev. E 90, 022142 (2014)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Role of connectivity and fluctuations in the nucleation of calcium waves in cardiac cells [Phys. Rev. E 92, 052715 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Lattice Boltzmann approach for complex nonequilibrium flows [Phys. Rev. E 92, 043308 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
See all related articles

Related Experiment Video

Updated: Jun 8, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Relative entropy as a universal metric for multiscale errors.

Aviel Chaimovich1, M Scott Shell

  • 1Department of Chemical Engineering, University of California, Santa Barbara, California 93106, USA.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|September 28, 2010
PubMed
Summary
This summary is machine-generated.

Relative entropy (Srel) is a key metric for assessing multiscale modeling success. It measures potential energy landscape differences, linking to coarse-graining errors in simulations.

More Related Videos

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Related Experiment Videos

Last Updated: Jun 8, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Area of Science:

  • Computational chemistry
  • Multiscale modeling
  • Statistical mechanics

Background:

  • Coarse-grained (CG) models simplify complex systems for simulations.
  • Linking CG models to first-principles (FP) calculations is crucial for accuracy.
  • Evaluating the success of CG models requires robust metrics.

Purpose of the Study:

  • To introduce relative entropy (Srel) as a fundamental indicator for multiscale studies.
  • To demonstrate Srel's ability to measure fluctuations between CG and FP potential energy landscapes.
  • To develop a theoretical framework linking Srel to coarse-graining errors.

Main Methods:

  • Theoretical development of the link between Srel and coarse-graining errors.
  • Analysis of potential energy landscape fluctuations.
  • Application to two simple case studies for validation.

Main Results:

  • Relative entropy (Srel) fundamentally indicates the success of linking CG and FP models.
  • Srel inherently quantifies fluctuations in potential energy landscape differences.
  • A theory is developed that generally connects Srel to coarse-graining inaccuracies.

Conclusions:

  • Srel serves as a vital metric for evaluating multiscale modeling.
  • The findings have significant implications for developing and refining CG models.
  • Relative entropy provides a pathway to more accurate and reliable simulations.