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

Correlation of Experimental Data01:23

Correlation of Experimental Data

256
Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...
256
Correlations02:20

Correlations

33.4K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
33.4K
Correlation01:09

Correlation

11.9K
In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
11.9K
Confidence Coefficient01:24

Confidence Coefficient

7.7K
The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
7.7K
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

1.7K
In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the...
1.7K
Correlation and Regression00:53

Correlation and Regression

1.3K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
1.3K

You might also read

Related Articles

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

Sort by
Same author

The relationship between coaching leadership behaviors and athletes' psychological fatigue: the mediating role of psychological resilience.

Frontiers in psychology·2026
Same author

An Oligomeric Additive Bridges Inner and Outer Helmholtz Planes to Enable Reversible Zn Anodes via Spatial and Functional Decoupling.

Angewandte Chemie (International ed. in English)·2026
Same author

MdPHYB2-MdPIF1 Module Regulates Transcription Factors to Mediate Light-Dependent ALA-Induced Apple Anthocyanin Biosynthesis.

Plant, cell & environment·2026
Same author

One-Pot Orthogonal Dual Functionalization of mi3 Self-Assembling Protein Nanoparticles via Sortase A and SpyCatcher/SpyTag Ligation.

Bioconjugate chemistry·2026
Same author

Evolution of Gas Film and Corresponding Drag Reduction Performance in Microchannels with Multi-Configuration Wall Microstructures.

Materials (Basel, Switzerland)·2026
Same author

Beyond the Echo Chamber: Upholding Clinical Objectivity in the Era of Sycophantic Large Language Models: Commentary on an article by Arthur J. Perry, BS, et al.: "Current Artificial Intelligence Large Language Models Exhibit Sycophantic Behavior in Orthopaedic Contexts".

The Journal of bone and joint surgery. American volume·2026

Related Experiment Video

Updated: Jul 25, 2025

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

2.6K

A New Correlation Measure for Belief Functions and Their Application in Data Fusion.

Zhuo Zhang1, Hongfei Wang1, Jianting Zhang2

  • 1School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China.

Entropy (Basel, Switzerland)
|June 28, 2023
PubMed
Summary

This study introduces a novel belief correlation measure, integrating uncertainty into Dempster-Shafer theory for better information processing. The new measure enhances multi-source data fusion by considering evidence credibility and usability.

Keywords:
Dempster–Shafer theorybelief correlation measureinformation fusionmulti-source datauncertainty

More Related Videos

A Photonic System for Generating Unconditional Polarization-Entangled Photons Based on Multiple Quantum Interference
00:07

A Photonic System for Generating Unconditional Polarization-Entangled Photons Based on Multiple Quantum Interference

Published on: September 5, 2019

8.5K
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.4K

Related Experiment Videos

Last Updated: Jul 25, 2025

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

2.6K
A Photonic System for Generating Unconditional Polarization-Entangled Photons Based on Multiple Quantum Interference
00:07

A Photonic System for Generating Unconditional Polarization-Entangled Photons Based on Multiple Quantum Interference

Published on: September 5, 2019

8.5K
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.4K

Area of Science:

  • Decision theory
  • Information fusion
  • Uncertainty quantification

Background:

  • Correlation measurement is crucial in Dempster-Shafer theory for uncertain information processing.
  • Existing correlation measures often neglect the impact of information uncertainty.
  • A comprehensive approach is needed to quantify the relevance between belief functions considering uncertainty.

Purpose of the Study:

  • To propose a new correlation measure for belief functions that incorporates information uncertainty.
  • To develop an information fusion method based on the novel correlation measure.
  • To enhance the accuracy and comprehensiveness of multi-source data fusion.

Main Methods:

  • Developed a belief correlation measure utilizing belief entropy and relative entropy.
  • Ensured the measure possesses key mathematical properties: probabilistic consistency, non-negativity, non-degeneracy, boundedness, orthogonality, and symmetry.
  • Proposed an information fusion method incorporating objective and subjective weights for evidence assessment.

Main Results:

  • The proposed belief correlation measure effectively quantifies the correlation between belief functions while accounting for uncertainty.
  • The associated information fusion method provides a more comprehensive assessment of evidence credibility and usability.
  • Numerical examples and application cases demonstrate the method's effectiveness in multi-source data fusion.

Conclusions:

  • The novel belief correlation measure offers a more comprehensive approach to quantifying relationships between belief functions by including uncertainty.
  • The proposed information fusion method improves multi-source data fusion by better evaluating evidence.
  • This work advances Dempster-Shafer theory applications in uncertain information processing and data fusion.