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相关概念视频

What is Population Genetics?01:25

What is Population Genetics?

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A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.
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Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

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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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Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

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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 +...
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Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

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A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
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Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

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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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Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

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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.
William S. Gosset (1876–1937) of the...
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相关实验视频

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Topographical Estimation of Visual Population Receptive Fields by fMRI
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人口遗传学的神经后部估计.

Jiseon Min, Yuxin Ning, Nathaniel S Pope

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    此摘要是机器生成的。

    神经后部估计 (NPE) 为人口遗传学提供了与近似贝叶斯计算 (ABC) 相对准确和高效的替代方案. 这种机器学习方法有效地从遗传数据中估计后向分布,克服了传统方法的局限性.

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    科学领域:

    • 人口遗传学 人口遗传学
    • 计算生物学 计算生物学
    • 机器学习 机器学习

    背景情况:

    • 基于模拟的推断方法,如近似贝叶斯计算 (ABC),在人口遗传学中很有价值,但在高维数据方面面临计算成本和局限性.
    • 监督机器学习 (ML) 提供了一个替代方案,但通常缺乏贝叶斯不确定性估计.

    研究的目的:

    • 引入和评估神经后部估计 (NPE) 作为一种结合ABC和监督ML强项的方法,用于种群遗传学.
    • 通过使用遗传数据来证明NPE在人口推断中的准确性,效率和适用性.

    主要方法:

    • 训练了一个神经网络,用于对人口遗传模型进行神经后部估计 (NPE).
    • 将NPE与现有推断方法进行比较,使用原始基因型和总结统计数据作为输入.
    • 应用于简单和复杂的人口模型的人口推理的NPE.

    主要成果:

    • 神经后部估计器在产生后部分布方面表现出高的准确性和效率.
    • 通过使用原始遗传数据和总结统计数据,NPE成功估计了后部分布.
    • 该方法在各种人口遗传场景中对人口推断有效.

    结论:

    • 神经后置估计 (NPE) 为复杂的人口遗传学推断提供了一种强大而通用的方法.
    • NPE克服了近似贝叶斯计算 (ABC) 和传统机器学习的关键局限性.
    • 提供了一个用户友好的工作流程,以促进在人口遗传学研究中采用NPE.