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相关概念视频

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...
724
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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Causality in Epidemiology01:21

Causality in Epidemiology

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Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
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Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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Uncertainty: Overview00:59

Uncertainty: Overview

595
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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Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

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Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
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相关实验视频

Updated: Jul 16, 2025

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
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基于功能方法的传染过程的不确定性分析.

Dunia López-Pintado1, Sara López-Pintado2, Iván García-Milán3

  • 1Economics Department, Universidad Pablo de Olavide, 41013, Seville, Spain. dlopez@upo.es.

Scientific reports
|September 19, 2023
PubMed
概括
此摘要是机器生成的。

由于随机传染,预测疾病传播具有挑战性. 最大的不确定性发生在流行病值,受网络密度和传染性的影响.

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科学领域:

  • 流行病学 流行病学
  • 网络科学 网络科学
  • 随机过程 随机过程

背景情况:

  • 预测疾病,产品或思想在人口中的传播本身很困难.
  • 对传染过程的有限观察阻碍了对未来事件的准确预测.
  • 传染的随机性质导致不可预测的结果,可能导致政策不准确.

研究的目的:

  • 分析传染过程中的不可预测性和不确定性.
  • 为功能数据开发一种新的非参数的方差测量方法.
  • 调查网络属性和过程传染性对不确定性的影响.

主要方法:

  • 对传染动态进行广泛的模拟研究.
  • 定义一种使用加权基于深度的中心区域的新型非参数方差测量方法.
  • 在小世界网络上应用敏感-感染-敏感 (SIS) 流行病学模型.

主要成果:

  • 传染的最大不确定性是在流行病值观察到的.
  • 网络密度和过程传染性显著且互补地影响传染不确定性.
  • 网络的随机性结构对传染不确定性的影响很小.

结论:

  • 了解传染不确定性对于准确的预测和有效的政策制定至关重要.
  • 开发的差异度提供了一个新的方法来量化功能传染数据中的不确定性.
  • 网络结构和传输动态是传染不可预测性的关键驱动因素.