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Sampling Methods: Overview01:06

Sampling Methods: Overview

272
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...
272
Random Sampling Method01:09

Random Sampling Method

11.0K
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...
11.0K
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

179
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...
179
Sampling Plans01:23

Sampling Plans

165
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...
165
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...
11.6K
Randomized Experiments01:13

Randomized Experiments

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
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Related Experiment Video

Updated: May 31, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

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Advanced Monte Carlo for Acquisition Sampling in Bayesian Optimization.

Javier Garcia-Barcos1, Ruben Martinez-Cantin1

  • 1Instituto Universitario de Investigacion en Ingenieria de Aragon (I3A), Universidad de Zaragoza, 50018 Zaragoza, Spain.

Entropy (Basel, Switzerland)
|January 24, 2025
PubMed
Summary
This summary is machine-generated.

Fully distributed Bayesian optimization (BO) enhances complex system optimization. Simplified Boltzmann sampling and Markov chain Monte Carlo (MCMC) methods improve acquisition sampling efficiency, especially with gradient information.

Keywords:
Bayesian optimizationGaussian processMCMC

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Area of Science:

  • Computational Science
  • Machine Learning
  • Optimization Algorithms

Background:

  • Optimizing complex systems requires efficient experiment selection, often hindered by cost and time.
  • Bayesian optimization (BO) offers sample efficiency but typically relies on sequential experiments.
  • Fully distributed BO is needed for parallel/asynchronous search, addressing privacy and resource limits.

Purpose of the Study:

  • To enhance fully distributed Bayesian optimization (BO) for complex systems.
  • To address challenges in sampling acquisition functions within distributed BO.
  • To improve the efficiency of parallel and asynchronous active search methods.

Main Methods:

  • Introduced a simplified Boltzmann sampling approach for fully distributed BO.
  • Analyzed various Markov chain Monte Carlo (MCMC) methods for acquisition sampling.
  • Implemented a numerically improved log Expected Improvement (EI) acquisition function.
  • Incorporated gradient information into MCMC sampling methods like MALA and CyclicalSGLD.

Main Results:

  • Gradient-informed MCMC methods (MALA, CyclicalSGLD) significantly improve acquisition sampling efficiency.
  • A mixture of proposals within the Metropolis-Hastings framework proved effective and simple.
  • The simplified Boltzmann sampling approach facilitates more efficient distributed BO.

Conclusions:

  • Gradient information is crucial for enhancing MCMC-based acquisition sampling in distributed BO.
  • Simplified Boltzmann sampling combined with advanced MCMC techniques offers a robust solution for parallel optimization.
  • The proposed methods improve the scalability and applicability of BO in resource-intensive settings.