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Random Sampling Method01:09

Random Sampling Method

14.0K
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...
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Cluster Sampling Method01:20

Cluster Sampling Method

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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...
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Sampling Plans01:23

Sampling Plans

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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...
832
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
240
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

1.8K
Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
1.8K
Sampling Methods: Overview01:06

Sampling Methods: Overview

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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...
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Related Experiment Video

Updated: Dec 31, 2025

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
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Gapsplit: efficient random sampling for non-convex constraint-based models.

Thomas C Keaty1,2, Paul A Jensen1,2,3

  • 1Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.

Bioinformatics (Oxford, England)
|January 9, 2020
PubMed
Summary

Gapsplit enhances random sampling for constraint-based models by focusing on under-sampled areas. This method ensures uniform coverage across linear, mixed-integer, and non-linear models, improving solution space exploration.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Constraint-based modeling is crucial for analyzing biological systems.
  • Exploring the full solution space of these models is computationally challenging.
  • Existing sampling methods may not adequately cover under-sampled regions.

Purpose of the Study:

  • To introduce Gapsplit, a novel method for generating random samples from constraint-based models.
  • To address the challenge of under-sampled regions in the solution space.
  • To ensure uniform coverage across diverse model types.

Main Methods:

  • Gapsplit employs a targeted approach to identify and sample under-sampled regions.
  • The algorithm is designed to work with linear, mixed-integer, and non-linear models.
  • Random samples are generated to provide comprehensive coverage of the solution space.

Main Results:

  • Gapsplit effectively generates random samples from convex and non-convex constraint-based models.
  • The method achieves uniform coverage across linear, mixed-integer, and general non-linear models.
  • Targeting under-sampled regions leads to a more thorough exploration of the solution space.

Conclusions:

  • Gapsplit offers a robust solution for sampling constraint-based models.
  • The tool enhances the analysis of biological systems by providing better solution space coverage.
  • Freely available source code facilitates adoption in computational biology research.