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相关概念视频

Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.1K
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...
4.1K
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.0K
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...
3.0K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.2K
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.
William S. Gosset (1876–1937) of the...
8.2K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

8.8K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
8.8K
Speciation Rates01:07

Speciation Rates

21.3K
Overview
21.3K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

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

578
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
578

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相关实验视频

Updated: Jul 23, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

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可扩展的贝叶斯分歧时间估计与比率转换.

Xiang Ji1, Alexander A Fisher2, Shuo Su3

  • 1Department of Mathematics, School of Science & Engineering, Tulane University, 6823 St. Charles Avenue, New Orleans, LA 70118, USA.

Systematic biology
|July 17, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的比率转换方法,以高效地估计从大型遗传学数据集的分歧时间. 新方法提高了病毒和物种进化研究的计算可行性和推断效率.

关键词:
贝叶斯的推理 贝叶斯的推理汉密尔顿式蒙特卡洛的 蒙特卡洛的差异时间估计差异时间估计.实际样本大小是有效的.病原体是一种病原体.人类遗传学 (phylogenetics) 是一个学科.比率转换的变化比率的变化

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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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相关实验视频

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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

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科学领域:

  • 人类遗传学和进化生物学
  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.

背景情况:

  • 估计分歧时间对于理解进化事件至关重要,从物种分歧到病毒传播.
  • 用数千个序列进行大规模的遗传学分析,由于相关的内部节点高度,会带来计算挑战.

研究的目的:

  • 开发一个可扩展的计算方法,用于分歧时间估计在植物遗传学.
  • 解决贝叶斯系遗传学研究中分析大序列数据集的计算不可行性.

主要方法:

  • 使用比例转换技术将内部节点高度重新映射到一个更易于计算的参数空间中.
  • 开发了线性时间算法来计算梯度和雅可比式相关术语,以进行高效的日志概率计算.
  • 哈密尔顿蒙特卡洛采样在贝叶斯框架内被用于推断分歧时间.

主要成果:

  • 比率转换方法对拉萨病毒,狂犬病病毒和珊瑚红藻数据集的推断效率提高了至少5倍.
  • 通过将混合效应分子时钟模型纳入埃博拉病毒分歧时间估计,实现了计算可行性.
  • 解决了西尼罗河病毒分歧时间估计中的混合问题,并揭示了某些类型的更清晰的多式分布.

结论:

  • 拟议的比率转换和相关算法提供了一个可扩展和高效的解决方案,用于大规模的基因分析中对差异时间的估计.
  • 这种方法提高了复杂的进化模型的计算可行性,导致了对进化历史的更强大的推断.
  • 这种方法在进化生物学中具有广泛的适用性,特别是对于病原体进化和物种多样化研究.