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相关概念视频

Sampling Methods: Overview01:06

Sampling Methods: Overview

309
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...
309
Sampling Plans01:23

Sampling Plans

180
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...
180
Sampling Distribution01:12

Sampling Distribution

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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...
12.4K
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
Cluster Sampling Method01:20

Cluster Sampling Method

11.9K
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...
11.9K
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

214
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...
214

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插即用分割的吉布斯采样器:在贝叶斯推理中嵌入深度生成先验.

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    此摘要是机器生成的。

    本研究介绍了一种新的随机插即用 (PnP) 算法,用于高效的后部分布采样. 它使用深度生成模型进行贝叶斯否定,提供与以前方法不同的置信区间.

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    科学领域:

    • 计算统计的计算统计.
    • 机器学习 机器学习
    • 图像处理 图像处理

    背景情况:

    • 后分布采样在贝叶斯推理中至关重要,但在计算上具有挑战性.
    • 现有的plug-and-play (PnP) 方法经常产生点估计,并且需要明确的先验.
    • 深度生成模型提供了强大的先前表示,但将其整合到采样中仍然是一个活跃的研究领域.

    研究的目的:

    • 引入一种新型的随机插即用 (PnP) 采样算法,以实现高效的后部分布采样.
    • 为了利用可变分割和深度生成模型来完成贝叶斯式否定任务.
    • 为了使贝叶斯估计器能够提供置信区间,提高不确定性量化.

    主要方法:

    • 开发了一种基于分割吉布斯采样 (SGS) 的随机PNP算法.
    • 灵感来自半方位分割 (HQS) 和交替方向乘法 (ADMM) 的方法.
    • 基于扩散的综合最先进的生成模型,用于贝叶斯否认子问题.

    主要成果:

    • 拟议的SGS算法有效地从后部分布中取样.
    • 该方法在预先训练的生成模型中隐式编码了先前信息,避免了明确的先前选择.
    • 与确定性PNP方法不同,随机方法提供了最小的额外计算成本的置信区间.

    结论:

    • 随机PNP采样策略对于图像处理任务是有效的.
    • 该算法提供了一种原则性的方法,将深度生成模型纳入贝叶斯推理中.
    • 这种方法通过提供置信区间来增强贝叶斯估计中的不确定性量化.