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Related Concept Videos

Sampling Distribution01:12

Sampling Distribution

Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
Construction of Root Locus01:15

Construction of Root Locus

The construction of a root locus involves several key steps to analyze and visualize the behavior of a system's poles with varying gain. The number of branches in the root locus equals the number of closed-loop poles and is symmetrical about the real axis.
For positive gain values, the root locus exists on the real axis to the left of an odd number of finite open-loop poles or zeros. The root locus starts at the open-loop poles and traces the paths of the closed-loop poles as the gain increases.
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

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...
Cluster Sampling Method01:20

Cluster Sampling Method

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...
Random Sampling Method01:09

Random Sampling Method

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...
Contaminants and Errors01:16

Contaminants and Errors

Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...

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Closed-form two-locus sampling distributions: accuracy and universality.

Paul A Jenkins1, Yun S Song

  • 1Computer Science Division, University of California, Berkeley, California 94720, USA.

Genetics
|September 10, 2009
PubMed
Summary
This summary is machine-generated.

Researchers derived new, accurate sampling formulas for population genetics, even with recombination. This advances understanding of genetic variation and aids in estimating population recombination rates.

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Area of Science:

  • Population Genetics
  • Evolutionary Biology
  • Computational Biology

Background:

  • Closed-form sampling formulas are crucial for population genetics but often intractable, especially with recombination.
  • Existing methods struggle with arbitrary recombination rates, limiting genetic analyses.
  • Previous work showed asymptotic formulas are possible for large population-scaled recombination rates (rho).

Purpose of the Study:

  • To generalize closed-form asymptotic sampling formulas to arbitrary finite-alleles mutation models.
  • To assess the accuracy of these formulas for specific mutation models.
  • To develop a practical method for classifying maximum-likelihood estimates of recombination rates.

Main Methods:

  • Asymptotic expansion of sampling formulas in inverse powers of the population-scaled recombination rate (rho).
  • Analytical computation of the first few terms of the expansion.
  • Extensive accuracy study using the two-locus parent-independent mutation model.
  • Application within the composite-likelihood framework.

Main Results:

  • The functional form of the asymptotic sampling formula is consistent across different mutation models for the computed terms.
  • The asymptotic formula demonstrates high accuracy for the two-locus parent-independent mutation model.
  • A simple, analytic condition was established for the existence of a finite maximum-likelihood estimate (MLE) of rho.

Conclusions:

  • Asymptotic sampling formulas provide a powerful and generalizable tool in population genetics.
  • The derived condition offers an efficient way to assess MLE existence for recombination rates.
  • This work facilitates more accurate and tractable analyses of genetic variation and recombination.