Jove
Visualize
Contact Us

Related Concept Videos

Uncertainty: Overview00:59

Uncertainty: Overview

1.4K
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
1.4K
Probability Histograms01:17

Probability Histograms

13.0K
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.
13.0K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.5K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.5K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

9.2K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
9.2K
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

1.2K
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
1.2K
Signal Flow Graphs01:18

Signal Flow Graphs

510
Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
In a signal-flow graph, branches denote the system's transfer functions, while nodes represent the signals. The direction of signal flow is indicated by arrows, with the corresponding...
510

You might also read

Related Articles

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

Sort by
Same author

A Novel DT40 Antibody Library for the Generation of Monoclonal Antibodies.

Virologica Sinica·2019
Same author

Pre-pregnancy maternal fasting plasma glucose levels in relation to time to pregnancy among the couples attempting first pregnancy.

Human reproduction (Oxford, England)·2019
Same author

Fast and accurate decoding of Raman spectra-encoded suspension arrays using deep learning.

The Analyst·2019
Same author

Pharmacokinetic and exposure-response analysis of pertuzumab in patients with HER2-positive metastatic gastric or gastroesophageal junction cancer.

Cancer chemotherapy and pharmacology·2019
Same author

Identification of diagnostic long non‑coding RNA biomarkers in patients with hepatocellular carcinoma.

Molecular medicine reports·2019
Same author

Ion-chelation based digital barcodes for multiplexing of a suspension array.

The Analyst·2019
Same journal

Two-phase Impulse Fluid on Particle Flow Map.

IEEE transactions on visualization and computer graphics·2026
Same journal

FGO-SLAM++: Real-time Geometry-Aware Gaussian SLAM with Continuous Opacity Field.

IEEE transactions on visualization and computer graphics·2026
Same journal

Blue Noise Dithering for Reservoir-based Spatio-temporal Importance Resampling.

IEEE transactions on visualization and computer graphics·2026
Same journal

ROS-GS: Relightable Outdoor Scenes With Gaussian Splatting.

IEEE transactions on visualization and computer graphics·2026
Same journal

MesoSplats: Texture Synthesis with Gaussian Splatting.

IEEE transactions on visualization and computer graphics·2026
Same journal

GLLA: A Unified Force-Directed Graph Layout Framework Supporting Local Adjustments.

IEEE transactions on visualization and computer graphics·2026
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: Dec 9, 2025

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
11:22

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

Published on: January 30, 2018

10.4K

Uncertainty Visualization of 2D Morse Complex Ensembles Using Statistical Summary Maps.

Tushar M Athawale, Dan Maljovec, Lin Yan

    IEEE Transactions on Visualization and Computer Graphics
    |September 8, 2020
    PubMed
    Summary
    This summary is machine-generated.

    Uncertainty in scalar fields complicates understanding Morse complexes. This study introduces statistical summary maps to visualize variations and positional uncertainties in 2D Morse complexes from uncertain data.

    More Related Videos

    Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
    07:12

    Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

    Published on: July 1, 2014

    12.6K
    Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
    08:51

    Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

    Published on: November 1, 2019

    5.9K

    Related Experiment Videos

    Last Updated: Dec 9, 2025

    Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
    11:22

    Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

    Published on: January 30, 2018

    10.4K
    Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
    07:12

    Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

    Published on: July 1, 2014

    12.6K
    Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
    08:51

    Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

    Published on: November 1, 2019

    5.9K

    Area of Science:

    • Topological Data Analysis
    • Scientific Visualization
    • Uncertainty Quantification

    Background:

    • Morse complexes are crucial for understanding scalar field topology in scientific visualization.
    • Data uncertainty inherent in scalar fields limits the structural interpretation of Morse complexes.
    • Existing methods struggle to represent the impact of data uncertainty on topological structures.

    Purpose of the Study:

    • To develop methods for visualizing uncertainty in ensembles of 2D Morse complexes derived from uncertain scalar fields.
    • To introduce novel statistical summary maps for quantifying structural variations and positional uncertainties.
    • To enhance the understanding of Morse complexes as structural abstractions in the presence of data uncertainty.

    Main Methods:

    • Generating ensembles of 2D Morse complexes from uncertain scalar fields.
    • Developing three types of statistical summary maps: probabilistic, significance, and survival maps.
    • Applying these maps to quantify structural variations and positional uncertainties of Morse complex features.

    Main Results:

    • Demonstrated the ability of statistical summary maps to characterize uncertain behaviors of gradient flows.
    • Quantified structural variations and positional uncertainties of Morse complexes in ensembles.
    • Provided new visual entities for understanding topological structures under data uncertainty.

    Conclusions:

    • The proposed statistical summary maps effectively visualize uncertainty in 2D Morse complexes.
    • This approach enhances the interpretability of topological structures in scalar fields with inherent data uncertainty.
    • The methods are validated on diverse simulation datasets including wind, flow, and ocean eddy.