Jove
Visualize
Contact Us

Related Concept Videos

Sampling Methods: Overview01:06

Sampling Methods: Overview

2.0K
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...
2.0K
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

1.8K
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...
1.8K
Sampling Distribution01:12

Sampling Distribution

16.4K
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...
16.4K
Cluster Sampling Method01:20

Cluster Sampling Method

13.9K
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...
13.9K
Sampling Plans01:23

Sampling Plans

829
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...
829
Random Sampling Method01:09

Random Sampling Method

14.0K
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...
14.0K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same journal

Inverse FIP effect plasma in the solar atmosphere: a synthesis of current understanding and new insights from AR 11967.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
Same journal

Signs of sulfur fractionation under high magnetic field strength.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
Same journal

First ionization potential fractionation of sulfur observed with spectral imaging of the coronal environment.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
Same journal

Chromospheric dynamics and turbulence regulate the solar FIP effect.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
Same journal

Exploring the link between wave activity in the photospheric velocity driver and the FIP bias in the solar corona.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
Same journal

Radiative hydrodynamic simulations of first ionization potential fractionation in solar flares.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Dec 30, 2025

High-speed Particle Image Velocimetry Near Surfaces
11:59

High-speed Particle Image Velocimetry Near Surfaces

Published on: June 24, 2013

33.7K

High-performance sampling of generic determinantal point processes.

Jack Poulson1

  • 1Hodge Star Scientific Computing, Toronto, M5G 2R3 Canada.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|January 21, 2020
PubMed
Summary
This summary is machine-generated.

Determinantal point processes (DPPs) now have efficient direct sampling schemes using LU and LDL factorizations for both non-Hermitian and Hermitian kernels. These methods improve performance, even in parallelized high-performance computing environments.

Keywords:
Kasteleyndeterminantal point processesfactorizationhigh-performance computingnon-Hermitianparallelsparse-direct

More Related Videos

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.6K
An Unbiased Approach of Sampling TEM Sections in Neuroscience
10:56

An Unbiased Approach of Sampling TEM Sections in Neuroscience

Published on: April 13, 2019

7.6K

Related Experiment Videos

Last Updated: Dec 30, 2025

High-speed Particle Image Velocimetry Near Surfaces
11:59

High-speed Particle Image Velocimetry Near Surfaces

Published on: June 24, 2013

33.7K
Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.6K
An Unbiased Approach of Sampling TEM Sections in Neuroscience
10:56

An Unbiased Approach of Sampling TEM Sections in Neuroscience

Published on: April 13, 2019

7.6K

Area of Science:

  • Computational Mathematics
  • Machine Learning
  • High-Performance Computing

Background:

  • Determinantal Point Processes (DPPs) model repulsive particle distributions and enhance diversity in recommender systems.
  • Standard DPP sampling involves costly spectral decomposition, limiting applicability to Hermitian kernels.
  • Prior research explored LDL factorizations for DPP sampling, but with limited performance gains.

Purpose of the Study:

  • To develop efficient direct sampling schemes for both Hermitian and non-Hermitian DPP kernels.
  • To demonstrate the performance benefits of these new sampling methods, particularly in parallel computing settings.
  • To provide open-source software implementing these advanced DPP sampling techniques.

Main Methods:

  • Modified LU factorization for efficient direct sampling of non-Hermitian DPP kernels.
  • Modified LDL factorization for efficient direct sampling of Hermitian DPP kernels.
  • Experimental evaluation using dynamically scheduled, shared-memory parallelizations of dense and sparse-direct factorizations.

Main Results:

  • Trivial modifications of LU and LDL factorizations yield efficient direct sampling for DPPs.
  • New methods demonstrate comparable or superior performance to existing techniques, especially in parallel settings.
  • The Catamari software offers header-only C++14 implementations of these DPP samplers.

Conclusions:

  • LU and LDL factorization-based methods provide efficient and broadly applicable DPP sampling.
  • The developed parallelized algorithms achieve high performance in computational science applications.
  • The released Catamari software facilitates the use of advanced DPP sampling techniques.