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 Histograms01:17

Probability Histograms

11.2K
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.
11.2K
Region of Convergence of Laplace Tarnsform01:20

Region of Convergence of Laplace Tarnsform

533
The Region of Convergence (ROC) is a fundamental concept in signal processing and system analysis, particularly associated with the Laplace transform. The ROC represents an area in the complex plane where the Laplace transform of a given signal converges, determining the transform's applicability and utility.
Consider a decaying exponential signal that begins at a specific time. When deriving its Laplace transform, the time-domain variable is replaced with a complex variable. This...
533
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

419
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
419
Principal Moments of Area01:14

Principal Moments of Area

1.1K
In mechanics, the product of inertia and moments of inertia of area help to calculate the stability and performance of various structures and components. The coordinate transformation relations are used to calculate the moments and products of inertia for an area about the inclined axes. Further, the moments and products of inertia with respect to the principal axes can be determined using the moments and products of inertia about the inclined axes.
The principal moment of inertia axes are the...
1.1K
Relative Frequency Histogram01:14

Relative Frequency Histogram

5.4K
The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...
5.4K
Interpreting X̄ Charts01:13

Interpreting X̄ Charts

65
Interpreting x̄ charts, a type of control chart used in statistical process control helps monitor the variation in processes over time. The x̄ chart is based on the sample mean and allows for monitoring variations in the process mean over time. These charts are pivotal for quality assurance in manufacturing and other sectors.
An x̄ chart plots the values of individual measurements over time against control limits calculated from historical data. The central line...
65

You might also read

Related Articles

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

Sort by
Same author

Retraction notice to "Effect of soil texture and zinc oxide nanoparticles on growth and accumulation of cadmium by wheat; a life cycle study" [Environ. Res. 216 (2023)114397].

Environmental research·2026
Same author

Molecular Epidemiology of Non-polio Enterovirus: Insights From L20B Cell Line Adaptation From Children With Acute Flaccid Paralysis in Pakistan.

The Pediatric infectious disease journal·2026
Same author

Internet of Medical Things Enabled Multimodal Framework: Deep Machine Learning for Chronic Cardiac Disease Prediction in Healthcare 5.0.

Healthcare technology letters·2026
Same author

MapReduce-based deep learning framework for potato leaf disease detection in sustainable precision agriculture.

Scientific reports·2025
Same author

Barriers to and enablers of Pakistani pharmaceutical export to regulated markets: regulatory perspective.

Journal of pharmaceutical policy and practice·2025
Same author

Multi-criteria decision making approach for solar energy implementation using N-cubic fuzzy interaction aggregation operators.

Scientific reports·2025

Related Experiment Video

Updated: Jun 27, 2025

Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism
11:04

Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism

Published on: September 1, 2014

11.2K

Developing Bayesian EWMA chart for change detection in the shape parameter of Inverse Gaussian process.

Amara Javed1, Tahir Abbas1,2, Nasir Abbas3

  • 1Department of Statistics, Government College University Lahore, Lahore, Pakistan.

Plos One
|May 6, 2024
PubMed
Summary

Bayesian control charts enhance manufacturing process monitoring by effectively managing variability. These new EWMA charts outperform traditional methods in detecting process shifts, validated by aerospace industry data.

More Related Videos

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.0K
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

42.9K

Related Experiment Videos

Last Updated: Jun 27, 2025

Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism
11:04

Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism

Published on: September 1, 2014

11.2K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.0K
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

42.9K

Area of Science:

  • Statistical Process Control
  • Quality Engineering
  • Manufacturing Systems

Background:

  • Bayesian control charts offer advanced methods for process monitoring.
  • Parametric uncertainty in manufacturing benefits from Bayesian approaches.
  • Exponentially-weighted-moving-average (EWMA) charts are crucial for process variability control.

Purpose of the Study:

  • To design and evaluate Bayesian EWMA control charts for the shape parameter of the Inverse Gaussian distribution.
  • To investigate the impact of hyperparameters on Bayesian estimates and posterior risks.
  • To assess the performance of proposed charts against classical EWMA charts.

Main Methods:

  • Development of Bayesian EWMA control charts utilizing different loss functions.
  • Analysis of hyperparameter effects on Bayes estimates and posterior risks.
  • Performance evaluation using Average Run Length (ARL), Standard Deviation of Run Length (SDRL), and Median of Run Length (MRL).

Main Results:

  • Bayesian EWMA charts demonstrate superior performance compared to classical EWMA charts.
  • The proposed charts are highly effective in detecting shifts in the shape parameter.
  • Simulative studies and real-world aerospace manufacturing data validate the findings.

Conclusions:

  • Bayesian EWMA control charts provide an efficient and effective approach for process monitoring.
  • The proposed methods offer improved fault detection capabilities.
  • The application to aerospace industry data confirms the practical utility and effectiveness of the Bayesian approach.