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Related Concept Videos

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...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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 squares (OLS)...
Stratified Sampling Method01:16

Stratified 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. 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.
To choose a stratified sample, divide the population into groups called strata and then take a...
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

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

Estimating Population Mean with Unknown Standard Deviation

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 Guinness...
What is Population Genetics?01:25

What is Population Genetics?

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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Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Structurama: bayesian inference of population structure.

John P Huelsenbeck1, Peter Andolfatto, Edna T Huelsenbeck

  • 1Department of Integrative Biology, University of California, Berkeley, CA 94720, USA.

Evolutionary Bioinformatics Online
|June 24, 2011
PubMed
Summary
This summary is machine-generated.

Structurama infers population structure by calculating individual assignment probabilities to populations. This program uses a Gibbs algorithm and offers flexible models for population number and admixture, aiding genetic data analysis.

Keywords:
Bayesian estimaionDirichlet Process PriorMarkov chain Monte Carlopopulation structure

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Area of Science:

  • Population genetics
  • Computational biology
  • Bioinformatics

Background:

  • Understanding population structure is crucial in genetics for evolutionary and conservation studies.
  • Existing methods may lack flexibility in modeling population number and admixture.
  • Accurate inference of genetic ancestry and population relationships is essential.

Purpose of the Study:

  • To introduce Structurama, a novel program for inferring population structure.
  • To provide a flexible computational tool for analyzing genetic data and assigning individuals to populations.
  • To implement advanced statistical models for population genetics inference.

Main Methods:

  • Utilizes a Gibbs sampling algorithm for efficient Markov chain Monte Carlo (MCMC) sampling.
  • Implements four distinct models: fixed or random population number (Dirichlet process prior) and no admixture or admixture.
  • Processes allelic data across multiple loci for population assignment.

Main Results:

  • Generates sampled partitions of individuals into populations, weighted by posterior probabilities.
  • Provides methods to summarize MCMC output, including calculation of a 'mean' partition.
  • Enables robust inference of population structure from genetic data.

Conclusions:

  • Structurama offers a powerful and flexible approach to population structure inference.
  • The program's models accommodate complex genetic scenarios, including unknown population numbers and admixture.
  • Structurama facilitates a deeper understanding of genetic relationships and population dynamics.