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相关概念视频

Random Sampling Method01:09

Random Sampling Method

11.2K
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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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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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
66
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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Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
110
Cluster Sampling Method01:20

Cluster Sampling Method

12.0K
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...
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Improving the Success Rate of Protein Crystallization by Random Microseed Matrix Screening
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Improving the Success Rate of Protein Crystallization by Random Microseed Matrix Screening

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通过随机旋转常规简单单的生成MCMC建议.

Andrew J Holbrook1

  • 1Department of Biostatistics, University of California, Los Angeles, CA 90095, USA.

Journal of multivariate analysis
|October 6, 2023
PubMed
概括
此摘要是机器生成的。

简化的采样器是一种新的并行马尔科夫链蒙特卡洛 (MCMC) 方法. 它简化了提案选择,为各种目标分布和维度提供了显著的加快速度.

关键词:
毛毛的尺度,衡量自己的尺度.马尔科夫连锁蒙特卡罗的蒙特卡罗是一个正角群是指一个正角群.并行MCMCMC是一个MCMC.

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科学领域:

  • 计算统计学 计算统计学
  • 马尔科夫链蒙特卡洛 (MCMC) 方法

背景情况:

  • 马尔科夫链蒙特卡洛 (MCMC) 方法被广泛用于贝叶斯推理和复杂概率分布的采样.
  • 现有的MCMC算法可能遭受缓慢的融合和高计算成本,特别是在高维空间.

研究的目的:

  • 引入一类新的并行MCMC方法,称为简化抽样器.
  • 为多提案MCMC算法开发一个简化验收步骤.
  • 为了证明简化采样器的理论和实际效率增长.

主要方法:

  • 简化采样器在每次代时通过旋转连接到当前马尔科夫链状态的简体生成多个提议.
  • 研究了一个基于高斯的多变量对称的多提议机制.
  • 通过在simplex节点中选择与它们的目标密度值成比例来简化验收步骤.

主要成果:

  • 简化采样器本质上保持了提案之间的对称性,导致简化了接受步骤.
  • 一个基于高斯的多变量对称的多提议机制也实现了这种简化接受.
  • 这两种方法都显示出显著的理论和实际加速.
  • 通过使用常规实现,在各种维度和目标分布中观察到效率增长.

结论:

  • 简化采样器为MCMC采样提供了一种计算效率高且可并行化的方法.
  • 简化验收步骤是一个关键的创新,减少了计算开销.
  • 该方法显示了加速贝叶斯推理和其他MCMC应用的前景.