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

Structural Classification of Joints01:20

Structural Classification of Joints

3.8K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
3.8K
Functional Classification of Joints01:09

Functional Classification of Joints

4.4K
Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...
4.4K
Structural Joints: Synovial Joints01:16

Structural Joints: Synovial Joints

3.8K
Synovial joints are the most common type of joint in the body. A key structural characteristic for a synovial joint is the presence of a joint cavity. This fluid-filled space is where the articulating surfaces of the bones contact each other. Also, unlike fibrous or cartilaginous joints, the articulating bone surfaces at a synovial joint are not directly connected to each other with fibrous connective tissue or cartilage. This gives the bones of a synovial joint the ability to move smoothly...
3.8K
Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

3.0K
The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
The relation  between entropy and disorder can be illustrated with the example of the phase change of ice to water. In ice, the molecules are located at specific sites giving a solid state, whereas, in a liquid form, these molecules are much freer to move. The molecular arrangement has therefore become more randomized. Although the change in average...
3.0K
Standard Entropy Change for a Reaction03:00

Standard Entropy Change for a Reaction

20.8K
Entropy is a state function, so the standard entropy change for a chemical reaction (ΔS°rxn) can be calculated from the difference in standard entropy between the products and the reactants.
20.8K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

667
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
667

You might also read

Related Articles

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

Sort by
Same author

Analyzing the digital customer journey: a novel framework for sequential behavior modeling.

Scientific reports·2026
Same author

Sensor Fusion for Target Detection Using LLM-Based Transfer Learning Approach.

Entropy (Basel, Switzerland)·2025
Same author

A Structured Social Media Health Support Program after Bariatric Surgery.

Applied clinical informatics·2024
Same author

A Comparative Analysis of Discrete Entropy Estimators for Large-Alphabet Problems.

Entropy (Basel, Switzerland)·2024
Same author

Distinguishing the Leading Agents in Classification Problems Using the Entropy-Based Metric.

Entropy (Basel, Switzerland)·2024
Same author

Utilizing direct and indirect information to improve the COVID-19 vaccination booster scheduling.

Scientific reports·2024
Same journal

A Survey on Human-Centric Voice-Face Multimodal Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

IEEE transactions on neural networks and learning systems·2026
Same journal

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

IEEE transactions on neural networks and learning systems·2026
Same journal

Hierarchical Semantic Concept Modeling for Generalizable Myocardial Pathology Segmentation on Multisequence CMR Images.

IEEE transactions on neural networks and learning systems·2026
Same journal

Stability of Time-Varying Impulsive Systems With State-Dependent Delay and Its Application in Complex Networks.

IEEE transactions on neural networks and learning systems·2026
Same journal

Adaptive Learning Control of Uncertain Systems via Weight and Intrinsic Plasticity-Based Neural Networks.

IEEE transactions on neural networks and learning systems·2026
See all related articles
  1. Home
  3. Neural Joint Entropy Estimation.
  1. Home
  3. Neural Joint Entropy Estimation.

Related Experiment Video

Measuring Neuromuscular Junction Functionality
10:40

Measuring Neuromuscular Junction Functionality

Published on: August 6, 2017

18.1K

Neural Joint Entropy Estimation.

Yuval Shalev, Amichai Painsky, Irad Ben-Gal

    IEEE Transactions on Neural Networks and Learning Systems
    |September 26, 2022

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    This study introduces a new method for estimating entropy and related information measures, especially effective for small sample sizes. The approach leverages deep neural networks for improved accuracy in machine learning and data compression tasks.

    More Related Videos

    Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
    11:15

    Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

    Published on: June 27, 2013

    33.9K
    A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
    12:39

    A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

    Published on: January 18, 2020

    7.8K

    Related Experiment Videos

    Measuring Neuromuscular Junction Functionality
    10:40

    Measuring Neuromuscular Junction Functionality

    Published on: August 6, 2017

    18.1K
    Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
    11:15

    Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

    Published on: June 27, 2013

    33.9K
    A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
    12:39

    A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

    Published on: January 18, 2020

    7.8K

    Area of Science:

    • Information Theory
    • Machine Learning
    • Statistics

    Background:

    • Estimating entropy for discrete random variables is crucial in information theory, machine learning, statistics, and data compression.
    • Existing methods often falter with small sample sizes relative to the variable's alphabet size.
    • Previous work by McAllester and Statos provides a foundation for entropy estimation.

    Purpose of the Study:

    • To develop a practical and accurate entropy estimation scheme for discrete random variables, particularly addressing limitations with small sample sizes.
    • To extend the proposed method for estimating related information-theoretic measures like conditional entropy and mutual information (MI).
    • To evaluate the performance and consistency of the new estimators across various applications.

    Main Methods:

    • Utilizing the generalization capabilities of cross-entropy estimation within deep neural networks (DNNs).
    • Extending existing work to create a family of novel estimators for entropy and related information-theoretic measures.
    • Applying the proposed estimators to large alphabet entropy estimation, MI estimation, conditional MI estimation, and transfer entropy (TE) estimation.

    Main Results:

    • The proposed estimators demonstrate strong consistency.
    • Improved performance was observed compared to existing methods across all tested scenarios, including large alphabet entropy, MI, conditional MI, and TE estimation.
    • The method effectively addresses the challenge of small sample sizes in entropy estimation.

    Conclusions:

    • The novel DNN-based approach offers a practical and accurate solution for entropy estimation, especially in low-sample regimes.
    • The developed estimators for conditional entropy and mutual information show significant promise for applications like independence testing.
    • This work advances the field of information-theoretic measure estimation with broad applicability in machine learning and data analysis.