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Uncertainty: Overview00:59

Uncertainty: Overview

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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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Prediction Intervals01:03

Prediction Intervals

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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. 
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Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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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...
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Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

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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...
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Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

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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...
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Neural Regulation01:37

Neural Regulation

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Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
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Related Experiment Video

Updated: Jan 17, 2026

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
14:55

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

4.2K

Uncertainty-Aware Parking Prediction Using Bayesian Neural Networks.

Alireza Nezhadettehad1, Arkady Zaslavsky1, Abdur Rakib2

  • 1School of Information Technology, Deakin University, Melbourne, VIC 3125, Australia.

Sensors (Basel, Switzerland)
|September 19, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces Bayesian Neural Networks (BNNs) for more reliable parking availability predictions. These uncertainty-aware models significantly improve accuracy, especially with limited or noisy data in intelligent transportation systems.

Keywords:
Bayesian neural networksaleatoric uncertaintycontext-aware predictionepistemic uncertaintyintelligent transportation systemsparking availability predictionuncertainty quantificationurban mobility

Related Experiment Videos

Last Updated: Jan 17, 2026

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
14:55

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

4.2K

Area of Science:

  • Intelligent Transportation Systems
  • Machine Learning
  • Uncertainty Quantification

Background:

  • Parking availability prediction is vital for reducing urban congestion.
  • Traditional deep learning models like LSTMs lack uncertainty quantification, limiting real-world robustness.
  • Bayesian Neural Networks (BNNs) offer a promising approach for modeling uncertainty.

Purpose of the Study:

  • To propose a BNN-based framework for parking occupancy prediction that models both epistemic and aleatoric uncertainty.
  • To enhance parking prediction accuracy and reliability by integrating contextual features.
  • To address the underutilization of BNNs in parking prediction due to computational complexity and lack of real-time context.

Main Methods:

  • Developed a Bayesian Neural Network (BNN) framework for parking occupancy prediction.
  • Incorporated contextual features (temporal, environmental) to improve uncertainty-aware predictions.
  • Evaluated the framework under data scarcity and synthetic noise injection.

Main Results:

  • BNNs outperformed traditional methods, achieving an average accuracy improvement of 27.4%.
  • Consistent performance gains were observed with limited (10-90% data) and noisy data.
  • Applying uncertainty thresholds (20%, 30%) enhanced decision-making reliability.

Conclusions:

  • Modeling both epistemic and aleatoric uncertainty significantly improves predictive performance in intelligent transportation systems.
  • BNN-based frameworks offer a robust solution for parking availability prediction, even with data limitations.
  • Uncertainty-aware approaches provide a foundation for future hybrid neuro-symbolic reasoning in intelligent transportation.