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相关概念视频

Unrealistic Optimism Bias01:30

Unrealistic Optimism Bias

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Unrealistic optimism bias is the tendency to overestimate the likelihood of positive outcomes. This cognitive bias makes individuals believe they are less likely to experience failures, setbacks, or risks and more likely to succeed than others. For example, people may assume they are less prone to health issues, accidents, or financial struggles than their peers, even when they share similar risk factors.One key component of this bias is the above-average effect, where individuals perceive...
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Decision Making: P-value Method01:09

Decision Making: P-value Method

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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
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Expected Value01:15

Expected Value

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The expected value is known as the "long-term" average or mean. This means that over the long term of experimenting over and over, you would expect this average. The expected average is represented by the symbol μ. It is calculated as follows:
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Prediction Intervals01:03

Prediction Intervals

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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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Regression Toward the Mean01:52

Regression Toward the Mean

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Randomized Experiments01:13

Randomized Experiments

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
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相关实验视频

Updated: Mar 6, 2026

An R-Based Landscape Validation of a Competing Risk Model
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An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

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通过在线强化学习中的样本内价值函数来缓解OOD过度乐观.

Wenhui Liu1, Kangyang Luo2, Zhijian Wu3

  • 1School of Data Science and Engineering, East China Normal University, No. 3663, North Zhongshan Road, Shanghai, 200062, China; College of Computer Science and Artificial Intelligence, Fudan University, No. 2005, Songhu Road, Shanghai, 200438, China.

Neural networks : the official journal of the International Neural Network Society
|March 4, 2026
PubMed
概括
此摘要是机器生成的。

线下强化学习 (RL) 面临着与分发之外的行动的挑战. 我们的样本预期值规范化 (IEVR) 方法有效地限制了这些行动,提高了基准任务的性能.

关键词:
预期价值规范化的规范化在样本学习学习.在线非线增强学习.强化学习是一种强化学习.

相关实验视频

Last Updated: Mar 6, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.7K

科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 线下强化学习 (RL) 从预先记录的数据中学习,而无需在线交互.
  • 在离线RL中评估分销外 (OOD) 行动是困难的,往往导致过度乐观的价值估计.
  • 现有的样本学习方法避免了OOD操作,但限制了概括能力.

研究的目的:

  • 开发一种方法,准确评估线下RL中的OOD行动.
  • 提高在样本学习方法的概括能力.
  • 为线下RL引入一种新的规范化技术.

主要方法:

  • 拟议的样本内预期价值规范化 (IEVR) 方法.
  • 使用样本中的预测值来限制OOD行动值.
  • 保存了标准的贝尔曼更新,用于在样本中的动作.
  • 提供了IEVR趋同的理论分析.

主要成果:

  • 实际上,IEVR使用样本内预测值有效地限制了OOD操作.
  • 理论分析证实了IEVR的收特性.
  • 实验结果显示,与现有方法相比,性能显著改善.
  • 在D4RL基准指标中,IEVR在各种任务中显示出有效性.

结论:

  • IEVR提供了一个简洁而有效的解决方案,用于在线RL中对OOD行动进行评估.
  • 样本内预测值作为一种有效的规范化方法.
  • 拟议的方法弥合了安全的样本学习和概括之间的差距.