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

292
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...
292
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

250
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
250
Parallel Processing01:20

Parallel Processing

638
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
638
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

242
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
242
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

682
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
682
Trial and Error and Algorithm01:12

Trial and Error and Algorithm

392
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
392

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

Sparse Bayesian multidimensional scaling(s).

Computational statistics·2025
Same author

Quantum Speedups for Multiproposal MCMC.

Bayesian analysis·2025
Same author

Tau drives cell specific functional isolation of the hippocampal formation.

bioRxiv : the preprint server for biology·2025
Same author

BEAST X for Bayesian phylogenetic, phylogeographic and phylodynamic inference.

Nature methods·2025
Same author

A SPATIALLY VARYING HIERARCHICAL RANDOM EFFECTS MODEL FOR LONGITUDINAL MACULAR STRUCTURAL DATA IN GLAUCOMA PATIENTS.

The annals of applied statistics·2025
Same author

Random-Effects Substitution Models for Phylogenetics via Scalable Gradient Approximations.

Systematic biology·2024
查看所有相关文章

相关实验视频

Updated: Jan 17, 2026

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

11.9K

平行MCMC算法:理论基础,算法设计,案例研究.

Nathan E Glatt-Holtz1, Andrew J Holbrook2, Justin A Krometis3

  • 1Department of Statistics, Indiana University, Bloomington, IN 47405, USA.

Transactions of mathematics and its applications : a journal of the IMA
|September 22, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了并行马尔科夫链蒙特卡洛 (pMCMC) 算法的统一框架,开发了新的多提议方法,如多提议预先条件的克兰克-尼科尔森 (mpCN) 采样器,用于复杂的贝叶斯推理问题.

关键词:
贝叶斯统计反转是贝叶斯的统计反转.汉密尔顿蒙特卡洛 (HMC) 模型马尔科夫链蒙特卡洛 (pMCMC) 是一个大都会哈斯廷斯的核子.平行 (多个提案)图形处理单元 (GPU) 是指图形处理单元.高性能计算的高性能计算.预先调节的 克兰克·尼科尔森 (pCN)简化的采样器.

更多相关视频

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
07:49

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

7.4K
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

11.7K

相关实验视频

Last Updated: Jan 17, 2026

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

11.9K
Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
07:49

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

7.4K
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

11.7K

科学领域:

  • 计算统计学 计算统计学
  • 贝叶斯的推理是贝叶斯的推理.
  • 马尔科夫链 蒙特卡洛方法

背景情况:

  • 平行马尔科夫链蒙特卡洛 (pMCMC) 算法对于有效地探索复杂的概率分布至关重要.
  • 现有的单提议方法理论缺乏对多提议扩展的统一框架.
  • 对于高维贝叶斯推理中的可扩展和高效算法的需求正在增长.

研究的目的:

  • 为并行马尔科夫链蒙特卡洛 (pMCMC) 算法建立一个严格的测量理论框架.
  • 为多重提案验收机制推导出一般的标准,以确保ergodicity.
  • 开发新的pMCMC算法,包括一个多提议预先条件的Crank-Nicolson (mpCN) 采样器.

主要方法:

  • 为pMCMC算法开发"扩展相位空间"形式主义.
  • 对多重提案接受机制的一般标准的推导.
  • 识别和应用新的算法,包括多提案预先条件的Crank-Nicolson (mpCN) 采样器.
  • 数字案例研究涉及并行化和贝叶斯统计反转.

主要成果:

  • 一个统一的pMCMC算法的理论框架,包括各种现有和新的方法.
  • 多重提案验收机制的一般标准,以保证 ergodic 链.
  • 引入了新的算法,特别是多重提议预先条件的Crank-Nicolson (mpCN) 采样器.
  • 在具有复杂目标分布的高维贝叶斯反转问题中证明mpCN的有效性.

结论:

  • 拟议的框架为pMCMC方法提供了坚实的理论基础.
  • 新的mpCN算法显示出对解决具有挑战性的高维贝叶斯推理任务的重大前景.
  • pMCMC算法,特别是mpCN,非常适合并行计算架构和现代高性能计算.