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Related Concept Videos

Sampling Methods: Overview01:06

Sampling Methods: Overview

3.5K
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...
3.5K
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

3.4K
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...
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Sampling Plans01:23

Sampling Plans

1.0K
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...
1.0K
Bandpass Sampling01:17

Bandpass Sampling

552
In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
552
Design Example: Strain Gauge Bridge or Wheatstone Bridge01:15

Design Example: Strain Gauge Bridge or Wheatstone Bridge

1.1K
The utilization of strain gauges as transducers for converting mechanical strain into electrical signals is a common practice in various engineering applications. These strain gauges are frequently integrated into Wheatstone bridge circuits to accurately measure parameters such as force or pressure. Within this context, each element within the circuit exhibits a resistance that undergoes subtle variations when subjected to mechanical strain. The primary objective is to convert minuscule...
1.1K
Cluster Sampling Method01:20

Cluster Sampling Method

14.9K
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...
14.9K

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An Unbiased Approach of Sampling TEM Sections in Neuroscience
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A tutorial on bridge sampling.

Quentin F Gronau1, Alexandra Sarafoglou1, Dora Matzke1

  • 1Department of Psychology, University of Amsterdam, The Netherlands.

Journal of Mathematical Psychology
|December 5, 2017
PubMed
Summary
This summary is machine-generated.

Bridge sampling offers a reliable method for approximating the marginal likelihood in Bayesian statistics. This technique is valuable for complex models in fields like reinforcement learning.

Keywords:
Bayes factorHierarchical modelMarginal likelihoodNormalizing constantPredictive accuracyReinforcement learning

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Area of Science:

  • Bayesian Statistics
  • Computational Statistics
  • Mathematical Psychology

Background:

  • The marginal likelihood is crucial for Bayesian inference tasks like parameter estimation and model comparison.
  • Analytical computation of the marginal likelihood is often intractable, necessitating numerical approximation methods.

Purpose of the Study:

  • To provide a tutorial on bridge sampling, a numerical method for approximating the marginal likelihood.
  • To demonstrate the application and accuracy of bridge sampling for complex models.

Main Methods:

  • Introduced bridge sampling and related methods using the beta-binomial model as an example.
  • Applied bridge sampling to estimate the marginal likelihood for the Expectancy Valence (EV) model in reinforcement learning.

Main Results:

  • Bridge sampling successfully estimated the marginal likelihood for both single-participant and hierarchical EV models.
  • The method proved reliable and accurate for models of varying complexity.

Conclusions:

  • Bridge sampling is an accessible and effective tool for approximating marginal likelihoods.
  • Recommended for mathematical psychologists dealing with potentially high-dimensional models.