Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Precipitation Processes01:12

Precipitation Processes

332
The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...
332
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

239
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
239
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

4.9K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
4.9K
Random Error01:04

Random Error

733
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
733
Global Climate Change01:50

Global Climate Change

24.0K
Throughout its ~4.5 billion year history, the Earth has experienced periods of warming and cooling. However, the current drastic increase in global temperatures is well outside of the Earth’s cyclic norms, and evidence for human-caused global climate change is compelling. Paleoclimatology, the study of ancient climate conditions, provides ample evidence for human-caused global climate change by comparing recent conditions with those in the past.
24.0K
Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

1.6K
Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...
1.6K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

A Model of Interacting Navier-Stokes Singularities.

Entropy (Basel, Switzerland)·2022
Same author

Posterior National Institutes of Health Stroke Scale Improves Prognostic Accuracy in Posterior Circulation Stroke.

Stroke·2021
查看所有相关文章

相关实验视频

Updated: May 12, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.6K

一个统计学学习方法,以地中海旋风.

L Roveri1, L Fery2,3, L Cavicchia4

  • 1Dipartimento di Matematica, Università di Pisa, Largo Pontecorvo 5, 56127 Pisa, Italy.

Chaos (Woodbury, N.Y.)
|May 8, 2025
PubMed
概括

地中海气旋是极端天气事件. 贝叶斯算法 (隐性迪里克莱特分配) 使用风数据对这些旋风进行分类,使得在气候变化中能够更好地检测和跟踪.

更多相关视频

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

7.4K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.6K

相关实验视频

Last Updated: May 12, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.6K
Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

7.4K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.6K

科学领域:

  • 气象学和气候科学 气象学和气候科学
  • 计算统计学 计算统计学

背景情况:

  • 与热带气旋相比,人们对地中海气旋的了解很少.
  • 这些极端事件对气候变化严重影响的地区构成风险.
  • 准确地描述地中海旋风是一个挑战.

研究的目的:

  • 用一种新的算法方法对地中海旋风进行分类.
  • 为了使风检测和跟踪进行先进的统计分析.
  • 为了更好地了解地中海盆地的极端天气.

主要方法:

  • 贝叶斯算法的应用,特别是隐性迪里克莱特分配 (LDA).
  • 基于风速数据的旋风的分类.
  • 为后续的统计学习而减少维度.

主要成果:

  • 使用LDA.成功地对地中海旋风进行了分类.
  • 显著减少数据的维度.
  • 促进对风分析进行监督统计学习.

结论:

  • 贝叶斯算法为分类地中海旋风等复杂的气象现象提供了强大的工具.
  • 通过LDA方法,可以更有效地检测和跟踪这些事件.
  • 这种方法有助于理解气候变化背景下的旋风行为.