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相关概念视频

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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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...
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Sampling Plans01:23

Sampling Plans

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
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Random Sampling Method01:09

Random Sampling Method

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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 Theorem01:15

Sampling Theorem

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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.
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Sampling Distribution01:12

Sampling Distribution

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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...
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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蒙特卡洛采样器用于高效的网络推理.

Zeliha Kilic1, Max Schweiger2,3, Camille Moyer2,4

  • 1Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, Tennessee, United States of America.

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概括
此摘要是机器生成的。

这项研究引入了一种新的贝叶斯非参数框架,从快照数据推断出生物反应网络. 该方法有效地估计了网络结构和参数,克服了基因调节网络分析中的挑战.

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科学领域:

  • 系统生物学 系统生物学
  • 计算生物学 计算生物学
  • 生物物理学的生物物理.

背景情况:

  • 生物过程通常使用快照数据进行研究,这些数据可以是随机的,并且需要概率模型来进行网络推理.
  • 从快照数据推断潜在的反应网络,包括节点数和动力参数,由于数据不确定性和时间尺度的分离,因此存在重大挑战.

研究的目的:

  • 开发一个贝叶斯非参数框架,能够从快照数据同时估计生物反应网络中的节点和参数数量.
  • 解决现有的参数贝叶斯方法在处理大时间尺度分离和未知的网络结构方面的局限性.

主要方法:

  • 一个混合贝叶斯马尔科夫链蒙特卡罗 (MCMC) 采样器,结合了哈密尔顿式蒙特卡罗 (HMC),自适应性大都市黑斯廷斯 (AMH) 和并行和.
  • HMC用于高效的参数空间探索,AMH用于提出可能模型,并行温度用于提高采样效率.

主要成果:

  • 拟议的混合MCMC采样器有效地解决了从随机快照数据推断网络结构和参数的挑战.
  • 对模仿单分子RNA光在位杂交 (RNA-FISH) 数据的合成数据的证明应用.

结论:

  • 开发的贝叶斯非参数框架为从快照数据中学习生物网络的动态模型提供了强大的解决方案.
  • 这种方法推进了转录网络和类似生物系统的分析,从有限的数据中推断底层动态至关重要.