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Sampling Distribution01:12

Sampling Distribution

16.7K
Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
16.7K
Cluster Sampling Method01:20

Cluster Sampling Method

14.0K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
14.0K
Sampling Theorem01:15

Sampling Theorem

1.3K
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
1.3K
Poisson Probability Distribution01:09

Poisson Probability Distribution

11.7K
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...
11.7K
Probability Distributions01:32

Probability Distributions

11.8K
 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...
11.8K
Probability Histograms01:17

Probability Histograms

13.1K
A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
13.1K

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相关实验视频

Updated: Jan 14, 2026

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

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从高斯玻色子采样分布在未加权图上进行高效的经典采样.

Yexin Zhang1,2, Shuo Zhou1,2, Xinzhao Wang1,2

  • 1Center on Frontiers of Computing Studies, Peking University, Beijing, China.

Nature communications
|October 22, 2025
PubMed
概括
此摘要是机器生成的。

我们开发了新的马尔科夫链蒙特卡洛算法,用于图形上采样高斯玻色子采样 (GBS) 分布. 这些算法为量子优势研究提供了理论上的保证和实际的加速.

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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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相关实验视频

Last Updated: Jan 14, 2026

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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科学领域:

  • 量子信息科学 量子信息科学
  • 计算复杂性 计算复杂性
  • 统计物理 统计物理

背景情况:

  • 高斯玻色子采样 (GBS) 是证明量子计算优势的一个关键领域.
  • 在解决复杂的图形相关问题方面,GBS具有潜在的应用.
  • 从GBS分布进行高效的经典采样对于验证和比较至关重要.

研究的目的:

  • 提出新的马尔科夫链蒙特卡洛 (MCMC) 算法用于采样GBS分布.
  • 在未加权图上分析这些算法的理论混合时间.
  • 用这些算法来展示使用这些算法对图表问题的实际性能改进.

主要方法:

  • 开发了格劳伯动力学的双循环变体.
  • 使用精细的正则路径参数进行理论分析,以证明密度图的多项式混合时间.
  • 在高达256个顶点的无权重图形上进行数值实验.

主要成果:

  • 拟议的双循环格劳伯动力学实现了与GBS分布相匹配的静止分布.
  • 多项式混合时间已被证明是密集图的.
  • 与随机搜索和模拟回火相比,在最大-哈夫尼安和最密集的k子图问题上观察到高达10倍的性能改善.

结论:

  • 开发的MCMC算法为从GBS分布中进行经典采样提供了理论上的保证和实际上的优势.
  • 这项工作提升了在量子优势演示和图形问题解决中使用GBS的潜力.
  • 这些算法具有可扩展性,在相关图形问题上优于现有方法.