Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Probability Distributions

Probability Distributions

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...

What is a Mode?

What is a Mode?

The mode is one of the commonly used measures of a central tendency. It is defined as the most frequent value in a data set.
There can be more than one mode in a data set if multiple values have the same highest frequency. For instance, suppose that the Statistics exam scores of 20 students are: 50; 53; 59; 59; 63; 63; 72; 72; 72; 72; 72; 76; 78; 81; 83; 84; 84; 84; 90; 93. Here, the mode is 72, as it occurs most frequently, five times.
A data set with two modes is called bimodal. For example,...

Probability Laws

Probability Laws

Probability Histograms

Probability Histograms

A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.

Poisson Probability Distribution

Poisson Probability Distribution

A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...

Molecular Orbital Theory I

Molecular Orbital Theory I

Overview of Molecular Orbital Theory

You might also read

Related Articles

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

Sort by

Same author

Phase I PIANO trial-PIPAC-oxaliplatin and systemic nivolumab combination for gastric cancer peritoneal metastases: clinical and translational outcomes.

ESMO open·2024

Same author

Corrigendum to "Natural mutations of human XDH promote the nitrite (NO<sub>2</sub><sup>-</sup>)-reductase capacity of xanthine oxidoreductase: A novel mechanism to promote redox health?" [Redox Biol. 4 (67) (2023) 102864].

Redox biology·2023

Same author

Natural mutations of human XDH promote the nitrite (NO<sub>2</sub><sup>-</sup>)-reductase capacity of xanthine oxidoreductase: A novel mechanism to promote redox health?

Redox biology·2023

Same author

Phylo-taxogenomics of the genus Tautonia with descriptions of Tautonia marina sp. nov., Tautonia rosea sp. nov., and emended description of the genus.

Systematic and applied microbiology·2021

Same author

Gastrointestinal: Black esophagus.

Journal of gastroenterology and hepatology·2021

Same author

Crateriforma spongiae sp. nov., isolated from a marine sponge and emended description of the genus "Crateriforma".

Antonie van Leeuwenhoek·2021

Search research articles

Related Experiment Video

Updated: Mar 11, 2026

Automation of Mode Locking in a Nonlinear Polarization Rotation Fiber Laser through Output Polarization Measurements

Automation of Mode Locking in a Nonlinear Polarization Rotation Fiber Laser through Output Polarization Measurements

Published on: February 28, 2016

A classification scheme for edge-localized modes based on their probability distributions.

A Shabbir¹, G Hornung¹, G Verdoolaege¹

¹Department of Applied Physics, Ghent University, B-9000 Ghent, Belgium.

The Review of Scientific Instruments

|December 3, 2016

Summary

This study introduces an automated classification method for plasma phenomena with uncertain parameters, using probability distributions and nearest neighbors. The technique efficiently categorizes edge-localized modes (ELMs), saving expert time.

More Related Videos

Determination of the Excitation and Coupling Rates Between Light Emitters and Surface Plasmon Polaritons

Determination of the Excitation and Coupling Rates Between Light Emitters and Surface Plasmon Polaritons

Published on: July 21, 2018

Fabrication and Characterization of Disordered Polymer Optical Fibers for Transverse Anderson Localization of Light

Fabrication and Characterization of Disordered Polymer Optical Fibers for Transverse Anderson Localization of Light

Published on: July 29, 2013

Related Experiment Videos

Last Updated: Mar 11, 2026

Automation of Mode Locking in a Nonlinear Polarization Rotation Fiber Laser through Output Polarization Measurements

Automation of Mode Locking in a Nonlinear Polarization Rotation Fiber Laser through Output Polarization Measurements

Published on: February 28, 2016

Determination of the Excitation and Coupling Rates Between Light Emitters and Surface Plasmon Polaritons

Determination of the Excitation and Coupling Rates Between Light Emitters and Surface Plasmon Polaritons

Published on: July 21, 2018

Fabrication and Characterization of Disordered Polymer Optical Fibers for Transverse Anderson Localization of Light

Fabrication and Characterization of Disordered Polymer Optical Fibers for Transverse Anderson Localization of Light

Published on: July 29, 2013

Area of Science:

Plasma physics
Machine learning
Data analysis

Background:

Edge-localized modes (ELMs) are crucial plasma instabilities in fusion devices.
Accurate classification of ELM types (e.g., Type I, Type III) is essential for understanding and controlling fusion plasmas.
Existing classification methods can be labor-intensive and struggle with parameter uncertainties.

Purpose of the Study:

To develop an automated, robust classification scheme for plasma phenomena, particularly edge-localized modes (ELMs).
To handle scenarios with significant parameter uncertainties or stochastic quantities.
To reduce the manual effort required by experts for ELM type identification.

Main Methods:

Modeling parameters with probability distributions within a metric space.
Employing a nearest neighbor approach for classification.
Applying the framework to classify Type I and Type III ELMs in carbon-wall plasmas at JET.

Main Results:

Successful implementation of an automated classification scheme for ELM types.
Demonstrated effectiveness in handling parameters with significant uncertainties.
Achieved fast and standardized classification of Type I and Type III ELMs.

Conclusions:

The developed automated classification scheme is efficient and accurate for ELM identification.
The method's generality allows for application to other plasma phenomena.
This approach is expected to significantly aid fusion research by streamlining data analysis.