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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.

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...

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...

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

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...

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

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Visual Analysis of Prediction Uncertainty in Neural Networks for Deep Image Synthesis.

Soumya Dutta, Faheem Nizar, Ahmad Amaan

IEEE Transactions on Visualization and Computer Graphics

|May 29, 2024

Summary

Deep neural networks (DNNs) can be made more reliable for visualization tasks by estimating prediction uncertainty. This approach enhances model interpretability and generates higher-quality, diverse visual outputs for scientific applications.

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Area of Science:

Artificial Intelligence
Computer Vision
Data Visualization

Background:

Deep neural networks (DNNs) are increasingly used for complex visualization tasks.
Standard DNNs lack inherent mechanisms to quantify prediction uncertainty, limiting their trustworthiness.
Understanding prediction quality, confidence, robustness, and uncertainty is crucial for informed decision-making in scientific applications.

Purpose of the Study:

To demonstrate efficient methods for estimating prediction uncertainty and sensitivity in DNNs for visualization.
To compare and contrast different uncertainty estimation techniques for deep image synthesis.
To highlight the benefits of uncertainty-aware deep visualization models.

Main Methods:

Utilizing various methods to estimate prediction uncertainty and sensitivity of DNNs.
Applying these methods to deep image synthesis tasks.
Interactively comparing and contrasting the results of different uncertainty estimation techniques.

Main Results:

Uncertainty-aware deep visualization models produce informative, high-quality, and diverse illustrations.
Prediction uncertainty estimation improves the robustness of deep visualization models.
Enhanced interpretability of deep visualization models is achieved through uncertainty quantification.

Conclusions:

Integrating prediction uncertainty estimation enhances the practical utility of DNNs in scientific visualization.
Uncertainty-aware models offer superior quality and diversity in generated visualizations.
These advancements make deep visualization models more reliable and interpretable for visual analysis in science.