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相关概念视频

Binomial Probability Distribution01:15

Binomial Probability Distribution

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A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
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Probability Distributions01:32

Probability Distributions

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 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
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Uniform Distribution01:19

Uniform Distribution

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The uniform distribution is a continuous probability distribution of events with an equal probability of occurrence. This distribution is rectangular.
Two essential properties of this distribution are
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Central Limit Theorem01:14

Central Limit Theorem

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The central limit theorem, abbreviated as clt, is one of the most powerful and useful ideas in all of statistics. The central limit theorem for sample means says that if you repeatedly draw samples of a given size and calculate their means, and create a histogram of those means, then the resulting histogram will tend to have an approximate normal bell shape. In other words, as sample sizes increase, the distribution of means follows the normal distribution more closely.
The sample size, n, that...
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Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

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The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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Poisson Probability Distribution01:09

Poisson Probability Distribution

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A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
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Updated: Sep 12, 2025

Optimization of Processing of Tiebangchui with Highland Barley Wine Based on the Box-Behnken Design Combined with the Entropy Method
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安全隐藏的迪里克莱特分配.

Thijs Veugen1,2, Vincent Dunning1, Michiel Marcus1

  • 1Unit ICT, Strategy and Policy, TNO, The Hague, Netherlands.

Frontiers in digital health
|August 8, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种安全的,去中心化的方法,用于在不共享敏感文件的情况下训练隐性迪里克莱特分配 (LDA) 主题模型. 保护隐私的方法实现了与集中式方法相似的准确性.

关键词:
派利尔加密系统 Paillier加密系统沙米尔的秘密分享分享隐藏的迪里克莱特分配.安全的多方计算安全的多方计算主题建模主题建模

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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
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科学领域:

  • 计算机科学 计算机科学
  • 数据科学数据科学数据科学
  • 密码学 密码学 密码学 密码学

背景情况:

  • 主题建模,包括隐性迪里克莱特分配 (LDA),对于文档分析至关重要.
  • 集中主题建模需要访问所有文档,对敏感数据构成隐私风险.
  • 需要分散的方法来训练分布式,私有数据集的模型.

研究的目的:

  • 开发一种新的,分散的方法,以安全地培训LDA模型.
  • 通过使用先进的增强隐私技术来保护数据隐私.
  • 为安全计算引入新的加密原体.

主要方法:

  • 一个去中心化的协议,用于在没有数据共享的情况下训练LDA模型.
  • 整合增强隐私的技术,以实现安全的计算.
  • 在秘密共享和同态加密之间进行转换的加密方法的开发.
  • 创建了一种方法,可以从具有秘密权重的有限集合中绘制随机数.

主要成果:

  • 分散的LDA协议的准确性与传统的集中方法相美.
  • 该解决方案通过单词和主题的数量展示了线性可扩展性.
  • 训练一个5个主题和3000个单词的模型需要大约16个小时,使用1024位Paillier键.

结论:

  • 一种安全和保护隐私的去中心化LDA培训方法是可行的.
  • 拟议的加密构建块在安全计算中具有独立的应用.
  • 这种方法可以在敏感的分布式数据集上进行协作主题建模.