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Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

681
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...
681
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

517
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...
517
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

74
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
74
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

4.1K
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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Probability in Statistics01:14

Probability in Statistics

12.9K
Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
12.9K
Prediction Intervals01:03

Prediction Intervals

2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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相关实验视频

Updated: Jun 28, 2025

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
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从顺序数据中做出不准确的概率推理.

Arthur Prat-Carrabin1, Michael Woodford1

  • 1Department of Economics, Columbia University.

Psychological review
|April 18, 2024
PubMed
概括
此摘要是机器生成的。

人类对象在估计概率时表现出贝叶斯推理的系统偏差. 他们早期对证据表现出不充分的反应,后来表现出过度的反应,这表明了注意力的经济,而不是错误的信念.

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科学领域:

  • 认知心理学 认知心理学
  • 决策科学 决策科学 决策科学
  • 行为经济学是一种行为经济学.

背景情况:

  • 贝叶斯范式是人类推理的一个关键模型,但其适用于现实世界的行为是有争议的.
  • 了解人类如何根据证据更新信仰对于认知科学至关重要.

研究的目的:

  • 调查人类概率估计中的贝叶斯推理的系统偏离.
  • 确定这些偏差背后的认知机制,超越简单的不正确的先验或常见的贝叶斯模型.

主要方法:

  • 实验对象在观察连续实现后估计了二进制事件的概率.
  • 分析的重点是确定对证据的反应不足和反应过度的模式.
  • 一个"噪音计数"模型被用来复制观察到的行为模式.

主要成果:

  • 试验对象表现出"保守主义" (反应不足),只有很少的观察和过度反应,有更长的序列.
  • 估计中的自相关性表明不准确的信念表示和噪音传播.
  • 即使考虑到内部不准确性和错误的信念,偏差仍然存在.

结论:

  • 人类概率估计偏离贝叶斯推理,原因是注意力经济,而不仅仅是错误的信念.
  • 被试节省了对信息的关注和响应控制,同时保持了适合任务的响应.
  • "噪音计数"模型有效地捕捉了这些观察到的行为模式,突出了注意力经济在决策中的重要性.