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Related Concept Videos

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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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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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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Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

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A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Uncertainty Quantification of Network Inference with Data Sufficiency.

Bharat Singhal1, Jorge Luis Ocampo-Espindola2, K L Nikhil3

  • 1Department of Electrical and Systems Engineering, Washington University in St Louis, St. Louis, Missouri 63130, USA.

IEEE Transactions on Network Science and Engineering
|August 28, 2025
PubMed
Summary
This summary is machine-generated.

Determining data sufficiency is crucial for accurate network inference. This study introduces a statistical method using confidence intervals to quantify data variability, ensuring reliable network topology reconstruction.

Keywords:
Confidence intervalsNetwork inferenceNetwork topologyNonlinear oscillators

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Area of Science:

  • Complex systems science
  • Network science
  • Statistical inference

Background:

  • Network inference reconstructs system connectivity from data, vital for understanding physical, biological, and chemical systems.
  • Current data-driven methods often overlook the critical question of data sufficiency for accurate network topology.
  • Accurate network reconstruction requires sufficient data variability to reliably infer underlying structures.

Purpose of the Study:

  • To develop a statistical method for assessing data sufficiency in network inference.
  • To quantify the uncertainty in inferred network connectivity based on data variability.
  • To ensure that inferred network topologies accurately reflect the true underlying network structure.

Main Methods:

  • Utilizing parametric confidence intervals to define bounds for true connection strengths.
  • Developing a technique to assess data variability for network inference accuracy.
  • Leveraging uncertainty quantification for inferred connectivity.

Main Results:

  • The proposed statistical method effectively determines data sufficiency for network inference.
  • Validation on Kuramoto and Stuart-Landau oscillator networks demonstrates method accuracy.
  • Successful application to experimental electrochemical oscillator network data confirms predictive power.

Conclusions:

  • The developed data sufficiency technique is essential for reliable network inference.
  • This method enhances the trustworthiness of network topology reconstructions.
  • It provides a quantitative measure to ensure sufficient data for accurate system analysis.