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相关概念视频

Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

51
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
51
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

100
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...
100
Time-Series Graph00:54

Time-Series Graph

4.4K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.4K
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

101
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
101
State Space Representation01:27

State Space Representation

246
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
246
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

67
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...
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相关实验视频

Updated: Jul 27, 2025

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

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空间时间数据的得分驱动建模.

Francesca Gasperoni1, Alessandra Luati2, Lucia Paci3

  • 1MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.

Journal of the American Statistical Association
|June 7, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的统计模型,用于分析重尾的时空数据. 该模型在功能磁共振成像 (fMRI) 数据中强有力的识别自发大脑激活.

关键词:
多变量学生-t分布强大的过器.在SAR模型中的SAR模型.这是自发的,自发的.激活方式 激活方式功能磁力共振成像 (fMRI) 是一种功能共振成像.

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Trajectory Data Analyses for Pedestrian Space-time Activity Study
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A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
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相关实验视频

Last Updated: Jul 27, 2025

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

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Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Trajectory Data Analyses for Pedestrian Space-time Activity Study

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科学领域:

  • 统计 统计 统计 统计
  • 神经科学是一个神经科学.
  • 数据科学数据科学数据科学

背景情况:

  • 时空数据分析通常需要考虑重尾和复杂依赖的模型.
  • 功能性磁共振成像 (fMRI) 数据由于其高维度和固有的噪声,提出了独特的挑战.
  • 识别自发大脑活动对于理解休息状态大脑功能至关重要.

研究的目的:

  • 开发一种用于分析重尾分布的时空数据的新型统计模型.
  • 将模型应用于功能磁共振成像 (fMRI) 数据,以识别自发大脑激活.
  • 提供一种可靠的方法来估计在存在重尾噪声时的动态信号.

主要方法:

  • 开发一个同时的自回归得分驱动模型,其中包含自回归干扰.
  • 空间过过程的信号加噪声分解,具有多变量Student-t噪声分布.
  • 利用条件概率函数的得分来驱动时空变量信号的动态.

主要成果:

  • 为最大概率估计器推导一致性和非对称正常性.
  • 演示模型在重尾分布中为时空变化的位置提供可靠更新的能力.
  • 在休息状态fMRI数据中成功识别了自发大脑区域激活.

结论:

  • 建议的得分驱动模型为时空数据分析提供了一个强大的框架,特别是对于重尾分布.
  • 该模型有效地捕捉了fMRI数据中的空间和时间依赖性,从而能够识别自发激活.
  • 这种方法推进了复杂的神经成像数据和其他具有极端值的时空数据集的分析.