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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.

Uncertainty: Confidence Intervals

Uncertainty: Confidence Intervals

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 't,' or...

Propagation of Uncertainty from Systematic Error

Propagation of Uncertainty from Systematic Error

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 particular...

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...

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...

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Related Experiment Videos

An Uncertainty-Aware Ensemble Approach to Modeling Utility of Pseudolabels for Semisupervised Learning.

Jiaqi Wu, Junbiao Pang, Qingming Huang

IEEE Transactions on Neural Networks and Learning Systems

|June 9, 2026

Summary

This study introduces an uncertainty-aware ensemble structure (UES) to improve semisupervised learning (SSL) by dynamically weighting pseudolabels. UES enhances deep neural network performance in computer vision tasks by addressing confidence threshold limitations.

Related Experiment Videos

Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Semisupervised learning (SSL) often discards low-confidence pseudolabels.
This approach faces challenges with setting confidence thresholds and deep neural network (DNN) overconfidence.

Purpose of the Study:

To introduce an uncertainty-aware ensemble structure (UES) for more effective pseudolabel utilization in SSL.
To jointly model prediction uncertainty and confidence for assessing pseudolabel utility.

Main Methods:

Developed an uncertainty-aware ensemble structure (UES) that dynamically converts pseudolabel utility into sample weights (SWs).
UES employs architecture-agnostic metrics for seamless integration into various computer vision tasks.

Main Results:

UES improved DualPose performance by up to 7.29% in PCK on human pose estimation datasets.
UES boosted FixMatch accuracy by up to 1.05% on image classification benchmarks like CIFAR and SVHN.

Conclusions:

The proposed UES effectively addresses limitations in traditional SSL pseudolabeling strategies.
UES demonstrates consistent performance improvements across diverse computer vision tasks and datasets.