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

Applications of Integration to Probability Density Functions01:27

Applications of Integration to Probability Density Functions

58
Continuous probability distributions are used to model random variables that can take on any real value within a specified range. These variables do not take on isolated or countable values but rather exist on a continuum. For example, the height of an individual can be measured with increasing precision—such as 163.5 or 165.25 centimeters—demonstrating that height is a continuous random variable.The behavior of such variables is described using a probability density function (PDF),...
58
Stream Function01:20

Stream Function

2.1K
In two-dimensional incompressible fluid flow, the continuity equation is essential for ensuring mass conservation, meaning that any change in fluid entering or exiting a region is balanced by a corresponding change elsewhere. For incompressible flow, where density remains constant, this requirement simplifies to the condition that the divergence of the velocity field must be zero. Mathematically, this is expressed as,
2.1K
Probability Laws01:49

Probability Laws

44.3K
Overview
44.3K
Functional Classification of Joints01:09

Functional Classification of Joints

6.8K
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...
6.8K
Probability Distributions01:32

Probability Distributions

12.1K
 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...
12.1K
Applications of the Ideal Gas Law: Molar Mass, Density, and Volume03:43

Applications of the Ideal Gas Law: Molar Mass, Density, and Volume

63.4K
The volume occupied by one mole of a substance is its molar volume. The ideal gas law, PV = nRT,  suggests that the volume of a given quantity of gas and the number of moles in a given volume of gas vary with changes in pressure and temperature. At standard temperature and pressure, or STP (273.15 K and 1 atm), one mole of an ideal gas (regardless of its identity) has a volume of about 22.4 L — this is referred to as the standard molar volume.
63.4K

You might also read

Related Articles

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

Sort by
Same author

ARKG: Adversarially Residual Knowledge Generalization to Open-Set Domain Adaptation.

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

Integrated GC-MS and ATR-FTIR analysis of airborne microplastics captured by spider webs.

Scientific reports·2026
Same author

Random Spatiotemporal Sampled-Data Control for Reaction-Diffusion Neural Networks With Dwell-Time-Based Sojourn Probability.

IEEE transactions on cybernetics·2026
Same author

Distributed Topology Reconfiguration for Open Multiagent Systems via Algebraic Connectivity and Kirchhoff Index.

IEEE transactions on cybernetics·2026
Same author

Muscle Function and Bone Strength in Children and Adolescents with Tall Stature.

Hormone research in paediatrics·2026
Same author

Stochastic-Sampling-Based Event-Triggered Control for Switching Reaction-Diffusion Neural Networks.

IEEE transactions on cybernetics·2026

Related Experiment Video

Updated: Feb 2, 2026

Pattern-based Search of Epigenomic Data Using GeNemo
06:38

Pattern-based Search of Epigenomic Data Using GeNemo

Published on: October 8, 2017

5.4K

On the Parzen Kernel-Based Probability Density Function Learning Procedures Over Time-Varying Streaming Data With

Piotr Duda, Leszek Rutkowski, Maciej Jaworski

    IEEE Transactions on Cybernetics
    |November 20, 2018
    PubMed
    Summary
    This summary is machine-generated.

    We introduce a recursive kernel density estimator (KDE) to effectively track dynamic density changes in data streams. This novel approach ensures accurate tracking and convergence, even in nonstationary environments.

    More Related Videos

    Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
    06:22

    Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

    Published on: September 19, 2025

    499
    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    3.5K

    Related Experiment Videos

    Last Updated: Feb 2, 2026

    Pattern-based Search of Epigenomic Data Using GeNemo
    06:38

    Pattern-based Search of Epigenomic Data Using GeNemo

    Published on: October 8, 2017

    5.4K
    Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
    06:22

    Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

    Published on: September 19, 2025

    499
    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    3.5K

    Area of Science:

    • Machine Learning
    • Data Mining
    • Statistical Modeling

    Background:

    • Traditional kernel density estimators (KDE) excel in stationary environments but lack convergence properties when adapted for data streams.
    • Existing extensions for stream data often rely on heuristics, compromising theoretical guarantees.
    • Nonstationary environments pose challenges for density estimation due to evolving data distributions.

    Purpose of the Study:

    • To propose a recursive variant of the Parzen kernel density estimator (KDE) for tracking dynamic density changes in data streams.
    • To establish theoretical convergence properties for recursive KDEs in nonstationary environments.
    • To provide practical guidance on parameter selection for ensuring robust performance.

    Main Methods:

    • Development of a recursive kernel density estimator (KDE) tailored for concept drift tracking.
    • Mathematical proofs demonstrating weak (in probability) and strong (with probability one) convergence properties.
    • Analysis of bandwidth and learning rate selection to guarantee convergence.

    Main Results:

    • The proposed recursive KDE demonstrates perfect tracking properties as sample size increases.
    • Theoretical convergence is proven, addressing limitations of heuristic-based stream data methods.
    • Simulation results validate the algorithm's effectiveness for density estimation and classification in time-varying data streams.

    Conclusions:

    • The recursive concept drift tracking KDE offers a theoretically sound and effective method for analyzing dynamic data streams.
    • This approach overcomes limitations of traditional KDEs in nonstationary environments.
    • The algorithm shows strong performance in both density estimation and classification tasks over time.