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

Uncertainty: Overview

Uncertainty: Overview

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.

Propagation of Uncertainty from Random Error

Propagation of Uncertainty from Random Error

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...

You might also read

Related Articles

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

Sort by

Same author

Copper-mediated amidation of alkenylzirconocenes with acyl azides: formation of enamides.

Organic letters·2013

Same author

JARID1A, JMY, and PTGER4 polymorphisms are related to ankylosing spondylitis in Chinese Han patients: a case-control study.

PloS one·2013

Same author

[The risk factors of ventilator-associated pneumonia in newborn and the changes of isolated pathogens].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition·2013

Same author

A route to phase controllable Cu2ZnSn(S(1-x)Se(x))4 nanocrystals with tunable energy bands.

Scientific reports·2013

Same author

Efficacy of an infection control program in reducing ventilator-associated pneumonia in a Chinese neonatal intensive care unit.

American journal of infection control·2013

Same author

[Effect of different forms of inorganic nitrogen on the photodegradation of antipyrine in water].

Huan jing ke xue= Huanjing kexue·2013

Same journal

Distributionally Robust Feature Selection.

Advances in neural information processing systems·2026

Same journal

On the Identifiability of Hybrid Deep Generative Models: Meta-Learning as a Solution.

Advances in neural information processing systems·2026

Same journal

Unlocking hidden biomolecular conformational landscapes in diffusion models at inference time.

Advances in neural information processing systems·2026

Same journal

JADE: Joint Alignment and Deep Embedding for Multi-Slice Spatial Transcriptomics.

Advances in neural information processing systems·2026

Same journal

Learning to Route: Per-Sample Adaptive Routing for Multimodal Multitask Prediction.

Advances in neural information processing systems·2026

Same journal

Emergence and Evolution of Interpretable Concepts in Diffusion Models.

Advances in neural information processing systems·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 8, 2025

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

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

Published on: November 30, 2022

Topology-Aware Uncertainty for Image Segmentation.

Saumya Gupta¹, Yikai Zhang², Xiaoling Hu^1,3

¹Stony Brook University, NY, USA.

Advances in Neural Information Processing Systems

|November 1, 2024

Summary

This summary is machine-generated.

This study introduces a new method for estimating uncertainty in the segmentation of complex structures like blood vessels. It identifies error-prone areas for expert review, improving annotation accuracy.

More Related Videos

Evaporation-reducing Culture Condition Increases the Reproducibility of Multicellular Spheroid Formation in Microtiter Plates

Evaporation-reducing Culture Condition Increases the Reproducibility of Multicellular Spheroid Formation in Microtiter Plates

Published on: March 7, 2017

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Published on: February 15, 2022

Related Experiment Videos

Last Updated: Jun 8, 2025

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

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

Published on: November 30, 2022

Evaporation-reducing Culture Condition Increases the Reproducibility of Multicellular Spheroid Formation in Microtiter Plates

Evaporation-reducing Culture Condition Increases the Reproducibility of Multicellular Spheroid Formation in Microtiter Plates

Published on: March 7, 2017

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Published on: February 15, 2022

Area of Science:

Computer Vision
Medical Image Analysis
Topological Data Analysis

Background:

Segmenting curvilinear structures (vasculature, roads) is difficult due to weak signals and complex topology.
Semi-automatic annotation methods require expert proofreading, necessitating efficient uncertainty estimation.
Existing pixel-wise uncertainty maps are insufficient for topological structure verification.

Purpose of the Study:

To develop a structure-wise uncertainty estimation method for curvilinear structure segmentation.
To identify error-prone topological structures for targeted expert verification.
To improve the efficiency and accuracy of large-scale annotation tasks.

Main Methods:

Leveraged discrete Morse theory (DMT) for capturing and analyzing topological structures.
Proposed a joint prediction model for inter-structural uncertainty estimation considering neighboring elements.
Introduced Probabilistic DMT with a perturb-and-walk scheme for intra-structural uncertainty modeling.

Main Results:

The proposed method generates superior structure-wise uncertainty maps compared to existing approaches.
Demonstrated effectiveness on various 2D and 3D datasets.
Successfully identified uncertain topological structures for verification.

Conclusions:

Structure-wise uncertainty estimation is crucial for accurate segmentation of curvilinear structures.
The novel approach using DMT effectively models both inter- and intra-structural uncertainties.
This method significantly aids in accelerating and improving the reliability of expert annotation.