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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

101
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
101
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

615
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
615
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.7K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
1.7K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.1K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.1K
Randomized Experiments01:13

Randomized Experiments

7.2K
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...
7.2K
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

887
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
887

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

Multiatom Skeletal Editing to Access SeCN-Containing Bicyclo[1.1.1]pentanes with Antitumor Activity.

Organic letters·2026
Same author

Photocatalytic Decarboxylation of Carboxylic Acids to Construct Unnatural Amino Acids and Peptides-Containing Piperidine Rings.

The Journal of organic chemistry·2026
Same author

Condition-Controlled Selective Synthesis of Benzo[<i>b</i>]azocinones or 2-Alkenylquinolines <i>via</i> Formal [5 + 3] or [5 + 1] Annulation of 2-Alkenylanilines with Cyclopropenones.

Organic letters·2026
Same author

Gene expression profiling and characterization of black rot resistance in Dendrobium Sonia 'Earsakul': a comparison of the non-mutagenized control and the resistant mutagenized line.

BMC plant biology·2026
Same author

Overexpression of a low-affinity fructokinase MdFRK1 leads to enhanced accumulation of fructose in apple calli.

Plant physiology and biochemistry : PPB·2026
Same author

The value of a nomogram model combining ultrasonic features and blood inflammatory indicators in differentiating benign and malignant thyroid nodules.

Medicine·2026

相关实验视频

Updated: Sep 13, 2025

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

5.0K

超参数优化EM算法通过贝叶斯优化和相对.

Dawei Zou1,2, Chunhua Ma1, Peng Wang1,2

  • 1School of Information Engineering, Suihua University, Suihua 152061, China.

Entropy (Basel, Switzerland)
|July 29, 2025
PubMed
概括

超参数优化 (HPO) 有效调整机器学习模型,使用从证据最大化中衍生的EM算法. 这种方法证明了相关向量机和贝叶斯线性回归的快速收.

科学领域:

  • 机器学习 机器学习
  • 统计建模 统计建模

背景情况:

  • 超参数优化 (HPO) 对机器学习模型性能至关重要.
  • 超参数调节算法行为,而不是从训练数据中学习的.

研究的目的:

  • 通过证据函数最大化来导出用于超参数优化的代方程.
  • 为超参数重估提供数学和统计解释.

主要方法:

  • 使用零平均高斯权重先验来优化相关性向量机超参数.
  • 在贝叶斯线性回归中对超参数导出代重估方程.
  • 应用相对和贝叶斯优化来将分割方程分成E和M步骤.

主要成果:

  • 证明了EM算法的超参数优化的有效性.
  • 算法表现出快速的收.
  • 后面分布中的单一共变矩阵影响了概率的增加.

结论:

  • 该EM算法是有效的超参数优化,提供快速的融合.
  • 进一步的研究可能会解决单一共变矩阵对模型概率的影响.
关键词:
在EM算法中,EM算法证据的功能是证据的功能.超参数优化的优化相对的相对.

更多相关视频

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.4K
Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.3K

相关实验视频

Last Updated: Sep 13, 2025

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

5.0K
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.4K
Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.3K