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関連する概念動画

The Uncertainty Principle04:08

The Uncertainty Principle

32.0K
Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...
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Uncertainty in Measurement: Reading Instruments02:46

Uncertainty in Measurement: Reading Instruments

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Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
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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.
1.7K
Uncertainty in Measurement: Significant Figures03:34

Uncertainty in Measurement: Significant Figures

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All the digits in a measurement, including the uncertain last digit, are called significant figures or significant digits. Note that zero may be a measured value; for example, if a scale that shows weight to the nearest pound reads “140,” then the 1 (hundreds), 4 (tens), and 0 (ones) are all significant (measured) values.
83.1K
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

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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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Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters
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不確実性定量化を伴う多変量およびオンライン転移学習

Jimmy Hickey1, Jonathan P Williams1, Brian J Reich1

  • 1Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA.

Statistics in medicine
|February 4, 2026
PubMed
まとめ
この要約は機械生成です。

この研究では、過小評価されているグループの歯周病予後モデリングを改善するための新しいベイズ転移学習フレームワークを紹介します。強化された方法は、データプライバシーを損なうことなく正確な予測を保証し、歯科医療アプリケーションにとって重要です。

科学分野:

キーワード:
ベイズ転移学習歯科記録情報ベイズ事前分布オンライン学習人種バイアス

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  • 生物統計学;歯科研究;機械学習

背景:

  • 歯周炎は一般的な歯科疾患であり、治療されない場合は歯の喪失につながる可能性があります。測定の難しさから、歯周病予後の正確なモデリングは困難です。既存のモデルは、過小評価されている人口統計グループに適用されると、失敗したりリスクを伴ったりする可能性があります。

主な方法:

  • RECaSTベイズ転移学習フレームワークへの拡張を提案しました。共同多変量予後モデリングアプローチを開発しました。逐次データセットのためのオンライン手法を導入し、負の転移を軽減しました。

結論:

  • 新しいベイズ転移学習フレームワークは、歯周病予後予測の精度と信頼性を向上させます。この手法は、人口統計学的表現が重要なヘルスケアアプリケーションに特に価値があります。このアプローチは、堅牢な不確実性定量化を提供し、ドメイン間でデータを共有しないことでデータプライバシーを保証します。