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

Probability Distributions01:32

Probability Distributions

6.8K
 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
6.8K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.0K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.0K
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

2.3K
The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
2.3K
Uniform Distribution01:19

Uniform Distribution

4.8K
The uniform distribution is a continuous probability distribution of events with an equal probability of occurrence. This distribution is rectangular.
Two essential properties of this distribution are
4.8K
Sampling Distribution01:12

Sampling Distribution

12.3K
Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
12.3K
Choosing Between z and t Distribution01:25

Choosing Between z and t Distribution

2.8K
The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...
2.8K

You might also read

Related Articles

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

Sort by
Same author

Minimizing Computation and Communication Costs of Two-Sided Secure Distributed Matrix Multiplication under Arbitrary Collusion Pattern.

Entropy (Basel, Switzerland)·2024
Same author

Fundamental Limits of Coded Caching in Request-Robust D2D Communication Networks.

Entropy (Basel, Switzerland)·2024
Same author

A Numerical Study on the Capacity Region of a Three-Layer Wiretap Network.

Entropy (Basel, Switzerland)·2023
Same author

On the Asymptotic Capacity of Information-Theoretic Privacy-Preserving Epidemiological Data Collection.

Entropy (Basel, Switzerland)·2023

Related Experiment Video

Updated: Jun 14, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

989

Optimizing Distributions for Associated Entropic Vectors via Generative Convolutional Neural Networks.

Shuhao Zhang1, Nan Liu2, Wei Kang1

  • 1School of Information Science and Engineering, Southeast University, Nanjing 211189, China.

Entropy (Basel, Switzerland)
|August 29, 2024
PubMed
Summary

This study introduces a novel neural network algorithm to efficiently determine if entropy vectors are entropic. The method generates probability mass functions, improving upon existing network coding techniques.

Keywords:
Ingleton scoreIngleton violation indexconvolutional neural networksentropic regionentropic vectorsinner boundsnetwork codingneural networks

More Related Videos

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.0K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

523

Related Experiment Videos

Last Updated: Jun 14, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

989
Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.0K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

523

Area of Science:

  • Information Theory
  • Computer Science
  • Machine Learning

Background:

  • Characterizing the almost-entropic region is crucial for network coding but remains a difficult, open problem.
  • Existing methods for determining entropic vectors are computationally intensive and lack efficiency.

Purpose of the Study:

  • To develop a novel algorithm for determining the entropic nature of arbitrary vectors in the entropy space.
  • To parameterize and generate probability mass functions using neural networks for entropic vector analysis.
  • To improve upon existing methods for network coding problems and construct better inner bounds for the almost-entropic region.

Main Methods:

  • Utilizing neural networks, specifically convolutional neural networks, to parameterize and generate probability mass functions.
  • Training neural networks to minimize the normalized distance between a target vector and a generated entropic vector.
  • Implementing GPU acceleration for faster computation and optimization of the algorithm.
  • Optimizing the Ingleton score and Ingleton violation index to derive a new lower bound.

Main Results:

  • The proposed algorithm successfully determines the entropic nature of target vectors and obtains underlying distributions.
  • Empirical results show improved normalized distances and convergence performance compared to prior works.
  • A new lower bound for the Ingleton violation index was obtained.
  • The best known inner bound for the almost-entropic region with four random variables was constructed, measured by volume ratio.

Conclusions:

  • The developed neural network-based algorithm offers an efficient and effective approach to entropic vector determination.
  • This computer-aided method has significant potential for constructing achievable schemes in network coding problems.
  • The findings advance the understanding and characterization of the almost-entropic region in information theory.