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

Constraints and Statical Determinacy01:26

Constraints and Statical Determinacy

601
In structural engineering, the equilibrium of a system is not only determined by its equations of equilibrium but also with the help of constraints. Constraints refer to restrictions on the motion of a system. The proper combinations of constraints can minimize the total number of constraints needed to maintain a system in mechanical equilibrium. When this happens, the system is said to be statically determinate. For such systems, the unknown reaction supports can be estimated using equilibrium...
601
Introduction to Test of Independence01:21

Introduction to Test of Independence

2.2K
In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
2.2K
Deductive Reasoning01:16

Deductive Reasoning

55.2K
Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
For example, a researcher can deduce specific predictions...
55.2K
Inductive Reasoning00:59

Inductive Reasoning

60.4K
Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...
60.4K
Real-World Application of Classical Conditioning01:15

Real-World Application of Classical Conditioning

545
Classical conditioning not only includes the initial pairing of stimuli but also extends to more complex forms, such as higher-order conditioning. Higher-order conditioning involves creating associations beyond the primary conditioned stimulus, resulting in a chain of conditioned responses.
Higher-order, or second-order, conditioning occurs when a neutral stimulus becomes associated with an already established conditioned stimulus through repeated pairings. For instance, if a dog has been...
545
Contingency Table01:29

Contingency Table

2.5K
A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
2.5K

You might also read

Related Articles

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

Sort by
Same author

Causally Informative Entropic Inequalities within Families of Distributions with Shared Marginals.

Entropy (Basel, Switzerland)·2026
Same author

Stimulus-dependent relationships between behavioral choice and sensory neural responses.

eLife·2021
Same author

The functional organization of cortical feedback inputs to primary visual cortex.

Nature neuroscience·2018
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jun 23, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.8K

Causal Structure Learning with Conditional and Unique Information Groups-Decomposition Inequalities.

Daniel Chicharro1, Julia K Nguyen2

  • 1Artificial Intelligence Research Centre, Department of Computer Science, City, University of London, London EC1V 0HB, UK.

Entropy (Basel, Switzerland)
|June 26, 2024
PubMed
Summary
This summary is machine-generated.

This study enhances causal inference by developing new inequalities. These tools improve the ability to test causal structures, even with hidden variables and weaker data conditions, advancing causal discovery methods.

Keywords:
causal discoverycausal structurescausalitydata processing inequalitydirected acyclic graphsentropic inequalitieshidden variablesmarginal scenariosmutual informationstructure learningunique information

More Related Videos

Dissociation of the Confounding Influences of Expectancy and Integrative Difficulty Residing in Anomalous Sentences in Event-related Potential Studies
05:22

Dissociation of the Confounding Influences of Expectancy and Integrative Difficulty Residing in Anomalous Sentences in Event-related Potential Studies

Published on: May 9, 2019

5.4K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.3K

Related Experiment Videos

Last Updated: Jun 23, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.8K
Dissociation of the Confounding Influences of Expectancy and Integrative Difficulty Residing in Anomalous Sentences in Event-related Potential Studies
05:22

Dissociation of the Confounding Influences of Expectancy and Integrative Difficulty Residing in Anomalous Sentences in Event-related Potential Studies

Published on: May 9, 2019

5.4K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.3K

Area of Science:

  • Causal inference
  • Information theory
  • Statistical modeling

Background:

  • Causal structure constrains variable distributions via conditional independencies.
  • Hidden variables complicate causal structure identification from observed data.
  • Existing inequalities test causal compatibility but have limitations.

Purpose of the Study:

  • To expand the applicability of group-decomposition inequalities for causal structure testing.
  • To derive new inequalities under weaker conditions and with conditioning sets.
  • To incorporate hidden variables and data processing inequalities for enhanced causal inference.

Main Methods:

  • Derivation of generalized group-decomposition inequalities.
  • Application of data processing inequalities for conditional mutual information.
  • Extension of data processing inequalities to conditional unique information.

Main Results:

  • New group-decomposition inequalities are derived with weaker independence and group configuration requirements.
  • The framework is extended to include conditioning sets.
  • Constraints involving hidden variables are developed and converted into testable forms using data processing inequalities.

Conclusions:

  • The enhanced inequalities offer greater power for testing causal structures, particularly with hidden variables.
  • The framework accommodates more complex scenarios and data structures.
  • This work advances methods for causal discovery and understanding system dynamics.