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Related Concept Videos

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...
Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
Sampling Theorem01:15

Sampling Theorem

In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
Sampling Methods: Overview01:06

Sampling Methods: Overview

A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of sampling...
Determination of Expected Frequency01:08

Determination of Expected Frequency

Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...

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Related Experiment Video

Updated: Jun 9, 2026

A Tactile Automated Passive-Finger Stimulator (TAPS)
19:44

A Tactile Automated Passive-Finger Stimulator (TAPS)

Published on: June 3, 2009

Motif discovery using expectation maximization and Gibbs' sampling.

Gary D Stormo1

  • 1Department of Genetics, School of Medicine, Washington University, St. Louis, MO, USA. stormo@wustl.edu

Methods in Molecular Biology (Clifton, N.J.)
|September 10, 2010
PubMed
Summary
This summary is machine-generated.

Expectation maximization and Gibbs

Related Experiment Videos

Last Updated: Jun 9, 2026

A Tactile Automated Passive-Finger Stimulator (TAPS)
19:44

A Tactile Automated Passive-Finger Stimulator (TAPS)

Published on: June 3, 2009

Area of Science:

  • Computational biology
  • Bioinformatics
  • Genomics

Background:

  • Identifying transcription factor binding sites is crucial for understanding gene regulation.
  • Statistical methods are employed to detect motifs representing these binding sites within DNA sequences.

Purpose of the Study:

  • To compare Expectation Maximization (EM) and Gibbs' sampling for motif discovery.
  • To evaluate the strengths and weaknesses of deterministic vs. stochastic approaches in sequence analysis.

Main Methods:

  • Expectation maximization (EM): A deterministic algorithm for motif identification.
  • Gibbs' sampling: A stochastic algorithm for motif identification.
  • Comparative analysis of algorithm convergence and solution reliability.

Main Results:

  • EM guarantees the same result with identical starting parameters, requiring multiple runs for optimal solutions.
  • Gibbs' sampling can yield different results even with the same initial parameters, also necessitating multiple runs.
  • Multiple runs for both methods help assess the likelihood of achieving a global optimum.

Conclusions:

  • Both EM and Gibbs' sampling are valuable for motif discovery but require careful implementation with multiple runs.
  • Understanding the deterministic vs. stochastic nature of these algorithms is key to reliable transcription factor binding site identification.