Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Unrealistic Optimism Bias01:30

Unrealistic Optimism Bias

211
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...
211
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

3.9K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
3.9K
Weighted Mean00:57

Weighted Mean

6.2K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
6.2K
Hindsight Biases01:12

Hindsight Biases

4.2K
Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
4.2K
The Representativeness Heuristic02:13

The Representativeness Heuristic

16.7K
The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
16.7K
Probability in Statistics01:14

Probability in Statistics

22.2K
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...
22.2K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Sequential Gibbs posteriors with applications to principal component analysis.

Biometrika·2026
Same author

Scalable and robust regression models for continuous proportional data.

Journal of the American Statistical Association·2026
Same author

Local graph estimation with pathwise false discovery control.

Nature communications·2026
Same author

Bayesian Transfer Learning.

Statistical science : a review journal of the Institute of Mathematical Statistics·2026
Same author

Domain Adaptive Bootstrap Aggregating.

IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society·2026
Same author

Logistic-Beta Processes for Dependent Random Probabilities with Beta Marginals.

Bayesian analysis·2026
Same journal

Instrumental Variable Estimation of Marginal Structural Mean Models for Time-Varying Treatment.

Journal of the American Statistical Association·2026
Same journal

Semiparametric Joint Modeling for Survival Analysis with Longitudinal Covariates.

Journal of the American Statistical Association·2026
Same journal

Dimension Reduction for Large-Scale Federated Data: Statistical Rate and Asymptotic Inference.

Journal of the American Statistical Association·2026
Same journal

Facilitating Heterogeneous Effect Estimation via Statistically Efficient Categorical Modifiers.

Journal of the American Statistical Association·2026
Same journal

Nonparametric Density Estimation of a Long-Term Trend from Repeated Semicontinuous Data.

Journal of the American Statistical Association·2026
Same journal

Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Clinicogenomic Data.

Journal of the American Statistical Association·2026
查看所有相关文章

相关实验视频

Updated: Jan 16, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.9K

通过乐观地重新权衡数据来强化概率.

Miheer Dewaskar1, Christopher Tosh2, Jeremias Knoblauch3

  • 1Department of Mathematics and Statistics, University of New Mexico, Albuquerque, NM 87106.

Journal of the American Statistical Association
|September 26, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了乐观加权概率 (OWL),以解决模型错误规范造成的基于概率的推理脆弱性. OWL通过计算与假设的轻微偏差来强化统计模型,提高现实世界数据分析的可靠性.

关键词:
粗的贝叶斯 (Bayes) 是一个粗的贝叶斯数据污染数据污染情况混合模型的混合模型.模型的错误规范是错误的异常价值标志 异常价值标志 异常价值标志强大的推理推理.总变化距离的总变化距离

更多相关视频

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

15.0K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.3K

相关实验视频

Last Updated: Jan 16, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.9K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

15.0K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.3K

科学领域:

  • 统计 统计 统计 统计
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 基于概率的推论被广泛使用,但对模型错误规范很敏感.
  • 模型的错误规范,包括异常值或不正确的假设,可以显著影响推理结果,这个问题被称为"脆弱性".

研究的目的:

  • 在基于概率的推理中解决脆弱性问题.
  • 开发一种可靠的统计方法,正式解释少量的模型错误规范.

主要方法:

  • 通过选择在经验测量的附近的一个模型友好的数据生成过程,引入了乐观加权概率 (OWL).
  • 专注于总变化 (TV) 社区进行理论分析.
  • 开发了用于实际应用的估计算法.

主要成果:

  • 通过整合一个处理小模型错误规范的机制,OWL强化了最初的可能性.
  • 研究了OWL的理论特性.
  • 通过混合模型和回归的应用来证明方法.

结论:

  • 乐观加权概率 (OWL) 为传统的概率方法提供了一个强大的替代方案.
  • OWL有效地减轻了模型错误规范的影响,提高了统计推断的可靠性.
  • 拟议的方法对复杂的统计建模场景中的应用非常有希望.