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The Uncertainty Principle04:08

The Uncertainty Principle

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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

52.9K
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...
52.9K
Uncertainty: Overview00:59

Uncertainty: Overview

1.7K
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

83.1K
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

11.7K
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...
11.7K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

2.0K
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...
2.0K

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Updated: Feb 6, 2026

Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters
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Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters

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Aprendizaje por transferencia multivariante y en línea con cuantificación de la incertidumbre

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
Resumen
Este resumen es generado por máquina.

Este estudio presenta un nuevo marco de aprendizaje por transferencia bayesiano para mejorar el modelado de resultados periodontales en grupos subrepresentados. El método mejorado garantiza predicciones precisas sin comprometer la privacidad de los datos, lo cual es crucial para las aplicaciones de salud dental.

Palabras clave:
aprendizaje por transferencia bayesianoregistros dentalesprior bayesiano informativoaprendizaje en líneasesgo racial

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Área de la Ciencia:

  • Bioestadística
  • Investigación Dental
  • Aprendizaje Automático

Sus antecedentes:

  • La periodontitis, una afección dental común, puede provocar la pérdida de dientes si no se trata.
  • El modelado preciso de los resultados periodontales es un desafío debido a las dificultades de medición.
  • Los modelos existentes pueden fallar o plantear riesgos cuando se aplican a grupos demográficos subrepresentados.

Objetivo del estudio:

  • Extender el marco de aprendizaje por transferencia bayesiano RECaST para mejorar el modelado de resultados periodontales.
  • Abordar las disparidades en la representación dentro de los grupos demográficos para el modelado predictivo.
  • Desarrollar un método que mejore el rendimiento del modelo para poblaciones subrepresentadas sin compartir datos.

Principales métodos:

  • Se propuso una extensión al marco de aprendizaje por transferencia bayesiano RECaST.
  • Se desarrolló un enfoque de modelado de resultados multivariante conjunto.
  • Se introdujo un método en línea para conjuntos de datos secuenciales y se mitigó la transferencia negativa.

Principales resultados:

  • El método propuesto mejoró significativamente sobre el enfoque univariante anterior RECaST.
  • Demostró un rendimiento predictivo efectivo y una cuantificación de la incertidumbre en datos dentales simulados y reales.
  • Mitigó con éxito la transferencia negativa, protegiendo a los grupos subrepresentados de la aplicación perjudicial del modelo.

Conclusiones:

  • El novedoso marco de aprendizaje por transferencia bayesiano mejora la precisión y la fiabilidad de la predicción de resultados periodontales.
  • El método es particularmente valioso para aplicaciones en atención médica donde la representación demográfica es crítica.
  • El enfoque ofrece una cuantificación robusta de la incertidumbre y garantiza la privacidad de los datos al no compartir datos entre dominios.