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Systematic Sampling Method

Systematic 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.
Systematic sampling is one of the simplest methods...

Sampling Theorem

Sampling Theorem

In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.

Sampling Methods: Overview

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 Continuous Time Signal

Sampling Continuous Time Signal

In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...

Sampling Plans

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...

Sampling Methods: Sample Types

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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Updated: Jun 5, 2026

A Modular Microfluidic Technology for Systematic Studies of Colloidal Semiconductor Nanocrystals

A Modular Microfluidic Technology for Systematic Studies of Colloidal Semiconductor Nanocrystals

Published on: May 10, 2018

Accelerated stochastic sampling of discrete statistical systems.

Zsolt Bertalan¹, Hidetoshi Nishimori, Henri Orland

¹Department of Physics, Tokyo Institute of Technology, Oh-okayama, Meguro-ku, Tokyo 152-8551, Japan. zsolt@stat.phys.titech.ac.jp

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|January 15, 2011

Summary

This summary is machine-generated.

We developed a new method to speed up sampling in complex systems by modifying the master equation. This approach accelerates simulated annealing for optimization problems like the traveling salesman problem.

Related Experiment Videos

Last Updated: Jun 5, 2026

A Modular Microfluidic Technology for Systematic Studies of Colloidal Semiconductor Nanocrystals

A Modular Microfluidic Technology for Systematic Studies of Colloidal Semiconductor Nanocrystals

Published on: May 10, 2018

Area of Science:

Statistical physics
Computational physics
Optimization algorithms

Background:

Complex energy landscapes in statistical systems often exhibit long relaxation times, hindering efficient sampling.
Existing methods for continuous systems are not directly applicable to discrete degrees of freedom.
Accelerating stochastic sampling is crucial for various computational problems.

Purpose of the Study:

To propose a novel method for reducing relaxation time in discrete stochastic systems.
To generalize a previously developed platform for continuous systems to discrete systems.
To accelerate simulated annealing for optimization problems.

Main Methods:

The method starts from a master equation, generalized for discrete systems.
The master equation is transformed into an imaginary-time Schrödinger equation.
The Hamiltonian is modified by adding a projector to its known ground state.

Main Results:

The transformation demonstrably decreases relaxation time toward equilibrium.
Implementation in a kinetic Monte Carlo scheme accelerated simulated annealing by an order of magnitude for the traveling salesman problem.
Comparisons with exchange Monte Carlo for the 3D Ising spin glass were performed.

Conclusions:

The proposed method effectively reduces relaxation times in discrete stochastic systems.
This approach offers a significant acceleration for simulated annealing in optimization tasks.
The work represents a step towards accelerating stochastic sampling in generic complex systems.