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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.

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Uncertainty: Confidence Intervals

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Associative Learning

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Updated: Oct 4, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Aleatory-aware deep uncertainty quantification for transfer learning.

H M Dipu Kabir¹, Sadia Khanam², Fahime Khozeimeh¹

¹Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Australia.

Computers in Biology and Medicine

|February 8, 2022

Summary

This summary is machine-generated.

This study introduces an aleatory-aware deep uncertainty quantification (UQ) method for classification, addressing limitations in current deep UQ models. The novel approach quantifies uncertainty in deep neural networks (DNNs) for improved diagnostic reliability.

Keywords:

Aleatoric COVID Epistemic Heteroscedastic Patient referral Uncertainty

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Last Updated: Oct 4, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

Artificial Intelligence
Machine Learning
Medical Imaging Analysis

Background:

Deep neural networks (DNNs) lack credibility without uncertainty quantification (UQ).
Existing deep UQ classification models primarily capture epistemic uncertainty, neglecting aleatory uncertainty.
Reliable classification in medical imaging requires addressing both types of uncertainty.

Purpose of the Study:

To propose a novel aleatory-aware deep UQ method for classification problems.
To develop a system that quantifies both aleatory and epistemic uncertainty in DNN predictions.
To enhance the credibility and reliability of DNN outcomes in critical applications like medical diagnosis.

Main Methods:

Implemented transfer learning to train DNNs and collect numeric output posteriors.
Introduced an "opacity score" derived from K-nearest output posteriors to quantify aleatory uncertainty.
Employed an ensemble of DNNs with varied initializations and architectures (ResNet, DenseNet) to capture epistemic uncertainty.
Utilized pre-trained network features with fully connected layers for efficient uncertainty estimation.

Main Results:

The proposed method effectively quantifies aleatory uncertainty using the opacity score.
Epistemic uncertainty was captured by training multiple DNNs, revealing sensitivity to training parameters.
Demonstrated a patient referral framework leveraging the developed UQ method for improved clinical decision-making.
The opacity score reflects classification certainty, with probabilities converging for uncertain outcomes.

Conclusions:

The developed aleatory-aware deep UQ method enhances the reliability of DNNs in classification tasks.
This approach provides a more comprehensive understanding of prediction uncertainty, crucial for medical applications.
The opacity score offers a valuable metric for detecting uncertainty in X-ray image analysis.
The study provides open-source code for the proposed UQ method, facilitating further research and application.