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相关概念视频

Random Sampling Method01:09

Random Sampling Method

12.5K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures 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. Among the various sampling methods used by...
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Sampling Methods: Overview01:06

Sampling Methods: Overview

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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
535
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

450
Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
450
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
Simple...
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Random Variables01:09

Random Variables

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A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
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Cluster Sampling Method01:20

Cluster Sampling Method

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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...
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相关实验视频

Updated: Sep 17, 2025

Establishing Single-Cell Based Co-Cultures in a Deterministic Manner with a Microfluidic Chip
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集成多个硬币翻转装置,以进行高质量的随机抽样.

Brady Taylor1,2, J Darby Smith3, Shashank Misra3

  • 1Sandia National Laboratories, Albuquerque, NM, 87123, USA. btaylor@sandia.gov.

Scientific reports
|July 2, 2025
PubMed
概括

生成高质量的随机数对于人工智能和科学计算至关重要. 这项研究使用道二极管作为代币翻转设备,开发一个系统来产生可靠的随机比特流,用于概率计算应用.

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Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

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Last Updated: Sep 17, 2025

Establishing Single-Cell Based Co-Cultures in a Deterministic Manner with a Microfluidic Chip
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Establishing Single-Cell Based Co-Cultures in a Deterministic Manner with a Microfluidic Chip

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A Microfluidic Chip for ICPMS Sample Introduction
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科学领域:

  • 随机计算是一种随机计算.
  • 微电子设备工程 微电子设备工程
  • 应用物理学的应用物理学

背景情况:

  • 人工智能,科学计算和概率计算依赖于随机抽样,需要大量的随机数字.
  • 随机微电子设备,或代币翻转设备,为高速随机位生成提供了潜在的解决方案,但遭受类似的非理想性,如温度依赖和漂移.
  • 这些非理想性可以引入确定性,损害生成的随机比特流的质量.

研究的目的:

  • 为应对coinflip设备中非理想性的挑战,以生成高质量的随机位流.
  • 开发一个用于生产可靠和不可预测的随机位的系统,适合概率计算.
  • 为了证明这些比特流在蒙特卡洛近似中的实际应用.

主要方法:

  • 探索用于随机位生成的coinflip设备,特别是道二极管.
  • 实施控制循环以减轻温度依赖,并确保单个设备的公平比特生成.
  • 来自多个道二极管的比特流并行组合,以实现公平和不可预测的输出.

主要成果:

  • 一个控制循环成功地实现了,以适应道二极管硬币翻转设备的温度变化,产生公平的比特流.
  • 从单个道二极管中组合并行的比特流产生了高质量,公平和不可预测的随机比特流.
  • 生成的比特流通过蒙特卡洛对pi的近似来验证它们在概率计算中的适用性.

结论:

  • 多个coinflip设备的系统,当适当管理时,可以克服模拟非理想性,以产生高质量的随机位流.
  • 开发的方法为生成高级计算范式所需的大量随机数提供了一种可行的方法.
  • 成功的蒙特卡洛近似证明了这些工程随机位流的实际实用性.