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関連する概念動画

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.
976
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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不確実性 データの十分性によるネットワーク推論の定量化

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
まとめ

データの十分性を決定することは,正確なネットワーク推論に不可欠です. この研究は,信頼区間を使用してデータの変動性を定量化し,信頼性の高いネットワークトポロジー再構築を保証する統計的方法を導入します.

キーワード:
信頼区間ネットワーク推論ネットワークトポロジー非線形振動器

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Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
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Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
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科学分野:

  • 複雑なシステム科学
  • ネットワーク科学
  • 統計的推論

背景:

  • ネットワーク推論はデータからシステムの接続性を再構築し,物理的,生物的,化学的システムを理解するために不可欠です.
  • 現在のデータ主導の方法は,正確なネットワークトポロジーのデータ十分性の重要な問題をしばしば無視しています.
  • 正確なネットワーク再構築には,信頼性の高い基礎構造を推論するために十分なデータ変性が必要です.

研究 の 目的:

  • ネットワーク推論におけるデータの十分性を評価するための統計的方法を開発する.
  • データ可変性に基づいて推論されたネットワーク接続の不確実性を定量化します.
  • 推論されたネットワークトポロジーは,真の基盤のネットワーク構造を正確に反映することを保証する.

主な方法:

  • パラメタリック信頼区間を使用して,真の接続強さの境界を定義します.
  • ネットワーク推論の精度を評価するための技術を開発する.
  • 推論された接続性の不確実性の定量化

主要な成果:

  • 提案された統計的方法は,ネットワーク推論のためのデータ十分性を効果的に決定します.
  • キュラモトとスチュワート・ランドー振動器のネットワークでの検証は,方法の精度を示しています.
  • 実験用電気化学振動器のネットワークデータへの成功適用は,予測力を確認しています.

結論:

  • 開発されたデータ十分性技術は,信頼性の高いネットワーク推論に不可欠です.
  • この方法は,ネットワークトポロジーの再構築の信頼性を高めます.
  • 正確なシステム分析のための十分なデータを確保するための定量的な尺度を提供します.