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相关概念视频

Poisson Probability Distribution01:09

Poisson Probability Distribution

8.5K
A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
8.5K
Poisson's And Laplace's Equation01:25

Poisson's And Laplace's Equation

3.5K
The electric potential of the system can be calculated by relating it to the electric charge densities that give rise to the electric potential. The differential form of Gauss's law expresses the electric field's divergence in terms of the electric charge density.
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

128
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...
128
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

619
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...
619
Poisson's Ratio01:23

Poisson's Ratio

545
Poisson's ratio is a material property that indicates their stress response. It explains the connection between the elongation or compression a material undergoes in the direction of an applied force and the contraction or expansion it experiences perpendicular to that force. When a slender bar is loaded axially, it stretches in the direction of the force and contracts laterally. Poisson's ratio is the negative ratio of this lateral contraction to the axial elongation. The negative sign...
545
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

87
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...
87

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相关实验视频

Updated: Sep 14, 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

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波桑网络自回归的贝叶斯混合模型.

Elly Hung1, Anastasia Mantziou1, Gesine Reinert2

  • 1Department of Statistics, University of Warwick, Coventry, CV4 7AL UK.

Social network analysis and mining
|July 21, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的贝叶斯式Poisson网络自回归混合 (PNARM) 模型,用于分析网络上的计数时间序列数据. 该模型有效地处理异质动态,并集群具有类似行为的节点,改进疾病传播建模.

关键词:
贝叶斯混合模型的贝叶斯混合模型.在 COVID-19 疫情中,网络回归回归网络回归.波桑模型是什么?

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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

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相关实验视频

Last Updated: Sep 14, 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

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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

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科学领域:

  • 统计建模 统计建模
  • 网络分析 网络分析
  • 时间序列分析时间序列分析.

背景情况:

  • 多变量计数时间序列数据在各个领域很常见,例如流行病学.
  • 传统模型经常假设高斯式误差,这可能不适合计数数据.
  • 在网络上传播疾病的建模需要考虑空间关系和异质动态.

研究的目的:

  • 开发一个灵活的统计模型,用于在网络上结构化的计数时间序列数据.
  • 将网络结构纳入稀疏性,并适应异质节点动态.
  • 为了聚集表现出类似时间行为的节点.

主要方法:

  • 提出了一个贝叶斯式波桑网络自回归混合 (PNARM) 模型.
  • 从Poisson网络自回归,分组网络自回归和共同集群先验中结合了概念.
  • 利用网络拓来告知一个结构向量自回归模型.

主要成果:

  • PNARM模型提供了一个以原则为基础的贝叶斯方法,用于基于网络的计数时间序列.
  • 该模型通过网络结构强加稀疏性,与全向量自回归模型形成鲜明对比.
  • 它允许集群具有类似动态模式的节点.

结论:

  • PNARM模型提供了一个强大的框架来分析网络上的计数时间序列.
  • 它通过考虑网络结构和异质性来增强对疾病传播等过程的理解.
  • 这种方法有助于在网络中识别不同的行为集群.