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相关概念视频

Probability in Statistics01:14

Probability in Statistics

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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...
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Poisson Probability Distribution01:09

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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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Overview
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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.
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The Bewley lattice diagram, developed by L. V. Bewley, effectively organizes the reflections occurring during transmission-line transients. It visually represents how voltage waves propagate and reflect within a transmission line, making it easier to understand the complex interactions that occur.
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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.
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相关实验视频

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Stable DNA Motifs, 1D and 2D Nanostructures Constructed from Small Circular DNA Molecules
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在微小的概率格子上,列举.

Yoshinori Aono1, Phong Q Nguyen2

  • 1National Institute of Information and Communications Technology, 4-2-1, Nukui-Kitamachi, Koganei, 1848795 Tokyo Japan.

Japan journal of industrial and applied mathematics
|December 4, 2025
PubMed
概括
此摘要是机器生成的。

这项研究表明,当高斯启发式失败时,修剪格子计数可能比预测慢,特别是在成功概率低的情况下. 研究人员提出了更新的成本预测和格子计数算法的下限.

关键词:
极端的修剪 极端的修剪高斯的启发式启发式.格子格子是一个格子格子.修改后的成本预测预测

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科学领域:

  • 计算数学是指计算数学.
  • 数学理论是数的理论.
  • 密码学 密码学 密码学 密码学

背景情况:

  • 格子计数对于计算格子问题至关重要,使用基于树的算法.
  • 现有的算法面临相对于格子排名的超指数时间复杂性.
  • 极端修剪策略提供了指数加速度,但依赖于准确的成本预测.

研究的目的:

  • 为了调查切割格子计数的实际成本超过预测成本的场景.
  • 确定高斯启发式的失败是导致这种差异的原因.
  • 建议修改成本预测和格子计数中的下限讨论.

主要方法:

  • 分析在特定条件下修剪格子计数成本.
  • 确定高斯启发式在预测格子点计数方面的失败.
  • 修改成本预测模型的开发和更新下限讨论.

主要成果:

  • 经过实践证明,剪裁计数成本远远超过预测的实际情况.
  • 将这种差异与高斯启发式的失败联系在一起,因为在修剪时成功概率非常低.
  • 拟议修订的下限在加密相关设置中是20-30倍大.

结论:

  • 高斯启发式可以低估格子点计数,导致在削减的计数中不准确的成本预测.
  • 将搜索区域限制在子空间中被确定为可能的原因.
  • 更新的成本预测和下限是必要的,以便更可靠地分析削减格子计数.