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相关概念视频

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

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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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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
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When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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从总体观测中估计参数:一个基于瓦斯斯坦距离的序列蒙特卡洛采样器.

Chen Cheng1, Linjie Wen2, Jinglai Li3

  • 1School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China.

Royal Society open science
|August 11, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的贝叶斯推理方法,使用瓦瑟斯坦距离和连续的蒙特卡洛采样器来估计粒子系统中的参数,当只有聚合数据可用时.

关键词:
瓦斯斯坦的距离是瓦斯斯坦的距离没有概率的推理推理.参数估计的参数估计.连续的蒙特卡洛采样器

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科学领域:

  • 计算物理 计算物理
  • 统计推理 统计推理
  • 数据科学数据科学数据科学

背景情况:

  • 在粒子系统中,参数估计至关重要,但当只有汇总数据可用时,这是具有挑战性的,由于缺乏概率函数,这阻碍了传统的贝叶斯推理.
  • 现有的贝叶斯方法在总量级观测方面存在困难,限制了它们在涉及复杂粒子动态的现实世界场景中的应用.

研究的目的:

  • 开发一种新的贝叶斯推理框架,用于使用聚合的观测数据在粒子系统中进行参数估计.
  • 在处理聚合级数据时,解决贝叶斯推理中不可用的概率函数的挑战.

主要方法:

  • 一个基于瓦瑟斯坦距离 (WD) 的序列蒙特卡洛 (SMC) 采样器被开发来测量观察和模拟的粒子分布之间的相似性.
  • 拟议的方法整合了WD用于分布比较和SMC采样,以处理顺序可用的观测.

主要成果:

  • 基于WD的SMC采样器有效地使用聚合数据估计粒子系统中的参数,即使概率函数没有明确可用.
  • 该方法在两个现实世界的例子中展示了强大的性能,验证了其实际适用性.

结论:

  • 提出的瓦瑟斯坦基于距离的序列蒙特卡罗方法为聚合观测数据的粒子系统中贝叶斯参数估计提供了强大的解决方案.
  • 这种方法克服了传统方法的局限性,有效地利用聚合数据并处理显式概率函数的缺失.