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Related Concept Videos

Sample Size Calculation01:19

Sample Size Calculation

Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
Upsampling01:22

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
Contaminants and Errors01:16

Contaminants and Errors

Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
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Introduction to Scalers

Many familiar physical quantities can be specified completely by giving a single number and the appropriate unit. For example, "a class period lasts 50 min," or "the gas tank in my car holds 65 L," or "the distance between the two posts is 100 m." A physical quantity that can be specified completely in this manner is called a scalar quantity. The word "scalar" is a synonym for "number." Time, mass, distance, length, volume, temperature, and energy are some examples of scalar quantities.
Scalar...
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

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 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. 
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An Innovative Method for Exosome Quantification and Size Measurement
11:38

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Published on: January 17, 2015

Information Conversion, Effective Samples, and Parameter Size.

Xiaodong Lin1, Jennifer Pittman, Bertrand Clarke

  • 1X. Lin is with the Department of Mathematical Sciences, University of Cincinnati, University of Cincinnati, Cincinnati, OH, 45221 USA (e-mail: linxd@math.uc.edu ).

IEEE Transactions on Information Theory
|December 17, 2008
PubMed
Summary
This summary is machine-generated.

We introduce a method to create virtual data samples that minimize informational differences between Bayesian posterior densities. This technique transfers inferential power from dependent to independent models, optimizing Bayesian analysis.

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Area of Science:

  • Statistics
  • Bayesian Inference
  • Information Theory

Background:

  • Comparing posterior densities from different models and datasets is crucial for robust statistical inference.
  • Existing methods may not effectively transfer inferential power between dependent and independent data structures.

Purpose of the Study:

  • To develop a method for creating "virtual samples" that align posterior densities in an informational sense.
  • To enable the transfer of inferential power from dependent datasets to independent models.

Main Methods:

  • Calculating relative entropy between two posterior densities.
  • Minimizing this relative entropy over parameters of a second dataset to generate a virtual sample.
  • Applying the optimization to models with nuisance parameters, finite mixture models, and correlated data.

Main Results:

  • The optimization yields a virtual sample that makes the second posterior closely resemble the first.
  • Effective transfer of inferential power is demonstrated when moving from dependent to independent models.
  • The approach successfully determines effective parameter size in Bayesian hierarchical models.

Conclusions:

  • The proposed relative entropy minimization provides a powerful tool for Bayesian model comparison and data synthesis.
  • This method enhances the utility of Bayesian inference by enabling flexible data and model integration.
  • The technique offers a principled way to reconcile information from diverse data sources and model structures.