Jove
Visualize
Contact Us

Related Concept Videos

Accuracy, limits, and approximation01:28

Accuracy, limits, and approximation

960
Accuracy, limits, and approximations are common in many fields, especially in engineering calculations. These concepts are imperative for ensuring that a given value is as close as possible to its true value.
Accuracy is defined as the closeness of the measured value to the true or actual value. In engineering mechanics, repeated measurements are taken during theoretical or experimental analyses to ensure that the result is precise and accurate.
The accuracy of any solution is based on the...
960
Density00:56

Density

18.3K
Density is an important characteristic of substances, crucial in determining whether an object sinks or floats in a fluid. Its SI unit is kg/m3, and its cgs unit is g/cm3. The density of an object helps in identifying its composition, and also reveals information about the phase of the matter and its substructure. The densities of liquids and solids are roughly comparable, consistent with the fact that their atoms are in close contact. However, gases have much lower densities than liquids and...
18.3K
Density and Archimedes' Principle01:05

Density and Archimedes' Principle

8.1K
When a lump of clay is dropped into water, it sinks. But if the same lump of clay is molded into the shape of a boat, it starts to float. Because of its shape, the clay boat displaces more water than the lump and experiences a greater buoyant force, even though its mass is the same. The same holds true for steel ships. The average density of an object majorly determines if the object will float. If an object's average density is less than that of the surrounding fluid, it will float. The...
8.1K
Degree of Curvature and Radius of Curvature01:19

Degree of Curvature and Radius of Curvature

343
The degree of curvature and the radius of curvature are fundamental concepts in determining the sharpness or smoothness of a curve. The degree of curvature is a measure of how steeply a curve bends and can be determined using the chord basis or the arc basis. In the chord basis method, the degree of curvature is defined as the central angle subtended by a chord of 30.48 meters, helping in the calculation of the radius of the curve. The arc basis method defines the degree of...
343
Relation between Poisson's ratio, Modulus of Elasticity and Modulus of Rigidity01:15

Relation between Poisson's ratio, Modulus of Elasticity and Modulus of Rigidity

424
Deformation occurs in axial and transverse directions when an axial load is applied to a slender bar. This deformation impacts the cubic element within the bar, transforming it into either a rectangular parallelepiped or a rhombus, contingent on its orientation. This transformation process induces shearing strain. Axial loading elicits both shearing and normal strains. Applying an axial load instigates equal normal and shearing stresses on elements oriented at a 45° angle to the load axis.
424
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

214
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
214

You might also read

Related Articles

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

Sort by
Same author

Pullback Bundles and the Geometry of Learning.

Entropy (Basel, Switzerland)·2023
Same author

On the Geodesic Distance in Shapes <i>K</i>-means Clustering.

Entropy (Basel, Switzerland)·2020
Same author

Functional Decomposition for Bundled Simplification of Trail Sets.

IEEE transactions on visualization and computer graphics·2017
See all related articles
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 Experiment Video

Updated: Nov 27, 2025

Magnet Assisted Composite Manufacturing: A Flexible New Technique for Achieving High Consolidation Pressure in Vacuum Bag/Lay-Up Processes
09:41

Magnet Assisted Composite Manufacturing: A Flexible New Technique for Achieving High Consolidation Pressure in Vacuum Bag/Lay-Up Processes

Published on: May 17, 2018

13.8K

Approximation of Densities on Riemannian Manifolds.

Alice le Brigant1, Stéphane Puechmorel1

  • 1Ecole Nationale de l'Aviation Civile, Université de Toulouse, 31055 Toulouse, France.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary

This study explores extending statistical probability distributions to Riemannian manifolds, offering new methods for analyzing complex data beyond simple Euclidean spaces. It addresses challenges in defining and estimating these distributions for advanced statistical modeling.

Keywords:
Riemannian manifolddirectional densitiesexponential familygroup invariancequantization

More Related Videos

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

28.9K
Viscoelastic Characterization of Soft Tissue-Mimicking Gelatin Phantoms using Indentation and Magnetic Resonance Elastography
07:57

Viscoelastic Characterization of Soft Tissue-Mimicking Gelatin Phantoms using Indentation and Magnetic Resonance Elastography

Published on: May 10, 2022

2.4K

Related Experiment Videos

Last Updated: Nov 27, 2025

Magnet Assisted Composite Manufacturing: A Flexible New Technique for Achieving High Consolidation Pressure in Vacuum Bag/Lay-Up Processes
09:41

Magnet Assisted Composite Manufacturing: A Flexible New Technique for Achieving High Consolidation Pressure in Vacuum Bag/Lay-Up Processes

Published on: May 17, 2018

13.8K
Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

28.9K
Viscoelastic Characterization of Soft Tissue-Mimicking Gelatin Phantoms using Indentation and Magnetic Resonance Elastography
07:57

Viscoelastic Characterization of Soft Tissue-Mimicking Gelatin Phantoms using Indentation and Magnetic Resonance Elastography

Published on: May 10, 2022

2.4K

Area of Science:

  • Statistics
  • Differential Geometry
  • Data Science

Background:

  • Estimating probability distributions is fundamental in statistics.
  • Classical methods are limited to Euclidean spaces.
  • Real-world data often exists on more complex spaces like Riemannian manifolds.

Purpose of the Study:

  • To survey extensions of probability distributions to Riemannian manifolds.
  • To address challenges in generalizing classical statistical procedures.
  • To provide options for parametric and non-parametric estimation on manifolds.

Main Methods:

  • Review of existing theoretical frameworks for distributions on manifolds.
  • Exploration of potential generalizations of well-known density families.
  • Consideration of computational and theoretical obstructions.

Main Results:

  • Identified several viable approaches for extending probability distributions to Riemannian settings.
  • Highlighted the complexities arising from the lack of unique generalizations.
  • Provided a survey of options for both parametric and non-parametric estimation.

Conclusions:

  • Adapting statistical distributions to Riemannian manifolds is crucial for complex data analysis.
  • The study offers a valuable overview of current methods and challenges.
  • This work facilitates advanced statistical modeling on non-Euclidean data.