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Related Concept Videos

Sampling Methods: Overview01:06

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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. 
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Sampling Theorem01:15

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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.
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Sampling materials are classified into three main types: solid, liquid, and gas.
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In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
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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...
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An Unbiased Approach of Sampling TEM Sections in Neuroscience
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Well-Tempered Variational Approach to Enhanced Sampling.

Omar Valsson1,2, Michele Parrinello1,2

  • 1Department of Chemistry and Applied Biosciences, ETH Zurich , c/o USI Campus, Via Giuseppe Buffi 13, CH-6900, Lugano, Ticino, Switzerland.

Journal of Chemical Theory and Computation
|November 18, 2015
PubMed
Summary
This summary is machine-generated.

We introduce a new iterative scheme for enhanced sampling and free energy calculations. Using a well-tempered distribution significantly improves convergence compared to a uniform distribution in variational approaches.

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Area of Science:

  • Computational chemistry
  • Statistical mechanics
  • Molecular dynamics

Background:

  • Variational approach to enhanced sampling and free energy calculations requires a target distribution.
  • Previous methods sometimes suffer from poor convergence, especially for complex systems.

Purpose of the Study:

  • To introduce and evaluate an iterative scheme using a well-tempered distribution as a target distribution.
  • To improve the convergence of enhanced sampling and free energy calculations.

Main Methods:

  • Developed a simple iterative scheme to implement the well-tempered distribution.
  • Applied the scheme to calculate the free energy surface of alanine tetrapeptide.
  • Compared convergence using well-tempered versus uniform target distributions.

Main Results:

  • The well-tempered distribution significantly improved convergence for the alanine tetrapeptide system.
  • The uniform target distribution showed poor convergence in this case.
  • The iterative scheme proved effective in utilizing the well-tempered distribution.

Conclusions:

  • The well-tempered distribution is a preferred and recommended target distribution for the variational approach.
  • This method offers a more efficient route for enhanced sampling and free energy calculations.
  • The proposed iterative scheme enhances the applicability of the variational approach.