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

Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

699
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...
699
Probability Distributions01:32

Probability Distributions

7.1K
 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
7.1K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.1K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.1K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

73
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...
73
Probability in Statistics01:14

Probability in Statistics

13.2K
Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
13.2K
Poisson Probability Distribution01:09

Poisson Probability Distribution

8.2K
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...
8.2K

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

Inference in spreading processes with neural-network priors.

Physical review. E·2026
Same author

Dynamical cavity method for hypergraphs and its application to quenches in the k-XOR-SAT problem.

Physical review. E·2025
Same author

An epidemiological knowledge graph extracted from the World Health Organization's Disease Outbreak News.

Scientific data·2025
Same author

Integer traffic assignment problem: Algorithms and insights on random graphs.

Physical review. E·2025
Same author

Sampling with flows, diffusion, and autoregressive neural networks from a spin-glass perspective.

Proceedings of the National Academy of Sciences of the United States of America·2024
Same author

Dynamical phase transitions in graph cellular automata.

Physical review. E·2024

相关实验视频

Updated: Jul 10, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K

在随机网络上散布过程的贝叶斯-最佳推理.

Davide Ghio1, Antoine L M Aragon2, Indaco Biazzo2

  • 1Ide PHICS Laboratory, Ecole Polytechnique Fédérale de Lausanne (EPFL), Rte Cantonale, 1015 Lausanne, Switzerland.

Physical review. E
|November 18, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了一种传递信息的算法,用于推断网络传播动态,将流行病模型概括起来. 该算法在特定条件下实现贝叶斯-最佳性能,提供对网络科学和计算推理的见解.

更多相关视频

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.1K
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.2K

相关实验视频

Last Updated: Jul 10, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.1K
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.2K

科学领域:

  • 统计物理学的统计物理.
  • 网络科学 网络科学
  • 计算推理推理是指计算推理.

背景情况:

  • 网络上的传播过程对于理解流行病等现象至关重要.
  • 现有的模型 (例如SIR,SIS) 在捕捉复杂的动态方面存在局限性.
  • 从部分观测中推断出这些动态是一个重大挑战.

研究的目的:

  • 开发和分析用于网络传播过程的传递消息的推断算法.
  • 用尼希莫里条件来确定算法是否达到贝叶斯最佳性能.
  • 研究随机网络上的相位转换和算法融合.

主要方法:

  • 流行病模型 (SIR,SIS) 的一般化,用于传播过程.
  • 消息传递推断算法来自信念传播 (BP) 方程.
  • 分析使用尼希莫里条件来评估贝叶斯-最佳性.
  • 通过融合时间和初始化策略测试相位过渡.

主要成果:

  • 在大型参数区域中,BP算法证明了贝叶斯最佳性能,密切满足了尼西莫里条件.
  • 即使在中等系统大小中,也可以观察到算法收和最佳性.
  • 在其他参数区域,BP算法难以收,归因于有限大小的效应而不是相位过渡.

结论:

  • 开发的消息传递算法为推断网络传播动态提供了一种有效且通常是最佳的方法.
  • 尼希莫里条件在这些推理问题中作为贝叶斯-优度的可靠指标.
  • 了解依赖参数的性能和有限大小的效应对于有效应用算法至关重要.