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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...
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 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...
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...
Correlation of Experimental Data01:23

Correlation of Experimental Data

Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity, and...

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Related Experiment Video

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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

A method for efficiently sampling from distributions with correlated dimensions.

Brandon M Turner1, Per B Sederberg, Scott D Brown

  • 1Department of Psychology.

Psychological Methods
|May 8, 2013
PubMed
Summary
This summary is machine-generated.

Differential evolution Markov chain Monte Carlo (DE-MCMC) offers efficient Bayesian estimation for complex psychological models. This population MCMC algorithm outperforms conventional methods, especially with correlated data, improving parameter estimation for models like the linear ballistic accumulator.

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Area of Science:

  • Psychology
  • Computational Statistics
  • Cognitive Science

Background:

  • Bayesian estimation is crucial for understanding individual differences in psychology.
  • Conventional Markov chain Monte Carlo (MCMC) methods struggle with parameter estimation for complex models, particularly when the target distribution's covariance structure is unknown.
  • Inefficiency of traditional MCMC algorithms hinders practical application in psychological modeling.

Purpose of the Study:

  • To address the difficulties in Bayesian estimation for psychological models.
  • To introduce and advocate for the use of differential evolution Markov chain Monte Carlo (DE-MCMC) for efficient proposal generation.
  • To demonstrate the superior performance of DE-MCMC compared to conventional MCMC algorithms.

Main Methods:

  • Utilized a simulation study to compare DE-MCMC with conventional MCMC algorithms.
  • Investigated the impact of target distribution correlation on algorithm performance.
  • Applied DE-MCMC to fit a hierarchical linear ballistic accumulator model to response time data.

Main Results:

  • DE-MCMC performance remained unaffected by the correlation of the target distribution.
  • Conventional MCMC performance degraded significantly as target distribution correlation increased.
  • DE-MCMC efficiently fitted a complex hierarchical model that was challenging for conventional MCMC.

Conclusions:

  • DE-MCMC provides a robust and efficient solution for Bayesian parameter estimation in psychological models.
  • The algorithm's effectiveness is particularly notable in scenarios with high correlation in the target distribution.
  • DE-MCMC facilitates the application of complex hierarchical models to psychological data, such as response time analysis.