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Videos de Conceptos Relacionados

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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Updated: Jan 24, 2026

Topographical Estimation of Visual Population Receptive Fields by fMRI
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Topographical Estimation of Visual Population Receptive Fields by fMRI

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Estimación de la distribución posterior neuronal para genética de poblaciones

Jiseon Min, Yuxin Ning, Nathaniel S Pope

    bioRxiv : the preprint server for biology
    |January 23, 2026
    PubMed
    Resumen
    Este resumen es generado por máquina.

    La estimación de la distribución posterior neuronal (NPE) ofrece una alternativa precisa y eficiente a la Computación Bayesiana Aproximada (ABC) para la genética de poblaciones. Este enfoque de aprendizaje automático estima eficazmente las distribuciones posteriores a partir de datos genéticos, superando las limitaciones de los métodos tradicionales.

    Palabras clave:
    genética de poblacionesestimación de la distribución posterior neuronalcomputación bayesiana aproximadaaprendizaje automáticoinferencia demográficadatos genéticos

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    Área de la Ciencia:

    • Genética de Poblaciones
    • Biología Computacional
    • Aprendizaje Automático

    Sus antecedentes:

    • Los métodos de inferencia basados en simulación, como la Computación Bayesiana Aproximada (ABC), son valiosos en genética de poblaciones pero enfrentan un costo computacional y limitaciones con datos de alta dimensionalidad.
    • El aprendizaje automático supervisado (ML) ofrece una alternativa pero carece típicamente de estimaciones de incertidumbre bayesiana.

    Objetivo del estudio:

    • Introducir y evaluar la Estimación de la Distribución Posterior Neuronal (NPE) como un método que combina las fortalezas de la ABC y el ML supervisado para la genética de poblaciones.
    • Demostrar la precisión, eficiencia y aplicabilidad de la NPE en la inferencia demográfica utilizando datos genéticos.

    Principales métodos:

    • Se entrenó una red neuronal para realizar la Estimación de la Distribución Posterior Neuronal (NPE) para modelos de genética de poblaciones.
    • Se comparó la NPE con métodos de inferencia existentes utilizando genotipos brutos y estadísticas resumidas como entrada.
    • Se aplicó la NPE a la inferencia demográfica para modelos de población tanto simples como complejos.

    Principales resultados:

    • Los estimadores de la distribución posterior neuronal demostraron alta precisión y eficiencia en la obtención de distribuciones posteriores.
    • La NPE estimó con éxito las distribuciones posteriores utilizando tanto datos genéticos brutos como estadísticas resumidas.
    • El método resultó eficaz para la inferencia demográfica en varios escenarios de genética de poblaciones.

    Conclusiones:

    • La Estimación de la Distribución Posterior Neuronal (NPE) proporciona un enfoque potente y versátil para la inferencia compleja en genética de poblaciones.
    • La NPE supera las limitaciones clave de la Computación Bayesiana Aproximada (ABC) y el aprendizaje automático tradicional.
    • Se proporciona un flujo de trabajo fácil de usar para facilitar la adopción de la NPE en la investigación de genética de poblaciones.