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相关概念视频

Inertia Tensor01:24

Inertia Tensor

469
The concept of the inertia tensor is employed to depict the mass distribution and rotational inertia of a solid or rigid object. This tensor is expressed through a three-by-three matrix. Each component within this matrix corresponds to varying moments of inertia about specific axes.
The diagonal components of the inertia tensor matrix represent the moments of inertia concerning the principal axes of the object. These primary axes are defined as the axes where the object experiences the least...
469
Vector Algebra: Method of Components01:08

Vector Algebra: Method of Components

13.9K
It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...
13.9K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

498
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
498
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.5K
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
1.5K
Scalar and Vector Triple Products01:06

Scalar and Vector Triple Products

2.4K
Two vectors can be multiplied using a scalar product or a vector product. The resultant of a scalar product is scalar, while with vector products, the resultant is a vector. These rules of the scalar or vector product between two vectors can be applied to multiple vectors to obtain meaningful combinations. The scalar triple product is the dot product of a vector with the cross product of two vectors.
The scalar triple product is the dot product of a vector with the cross product of two vectors....
2.4K
Cartesian Form for Vector Formulation01:26

Cartesian Form for Vector Formulation

641
The Cartesian form for vector formulation is a process to calculate  the moment of force using the position and force vectors. The moment of force is defined as the cross-product of these vectors, making it a vector quantity. The Cartesian form of the position and force vectors involves unit vectors, which can be used to express the cross-product in determinant form.
641

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相关实验视频

Updated: Jul 1, 2025

Author Spotlight: Analgesic Effect of Tuina on Rat Models with Compression of the Dorsal Root Ganglion Pain
05:49

Author Spotlight: Analgesic Effect of Tuina on Rat Models with Compression of the Dorsal Root Ganglion Pain

Published on: July 14, 2023

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贝叶斯张量对张量的回归与高效计算.

Kunbo Wang1, Yanxun Xu1

  • 13400 N. Charles Street, Baltimore, MD 21218.

Statistics and its interface
|March 12, 2024
PubMed
概括
此摘要是机器生成的。

我们介绍了贝叶斯张量对张量回归方法,用于预测多维数组. 这种方法有效地估计了模型尺寸和参数,为复杂数据提供了卓越的性能和不确定性量化.

关键词:
分数贝叶斯因子 分数贝叶斯因子马尔科夫连锁蒙特卡罗的蒙特卡罗是一个张力器对张力器的回归.塔克尔分解的分解方法

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Image-based Lagrangian Particle Tracking in Bed-load Experiments

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相关实验视频

Last Updated: Jul 1, 2025

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科学领域:

  • 多变量统计的多变量统计.
  • 机器学习 机器学习
  • 数据科学是数据科学.

背景情况:

  • 张量回归对于分析多维数据至关重要.
  • 现有的塔克分解方法难以同时估计核心张量尺寸和参数.
  • 需要强大的方法来解决这些局限性.

研究的目的:

  • 提出一个新的贝叶斯张量-张量回归框架.
  • 开发用于同时估计模型尺寸和参数的方法.
  • 为了提供张量回归的不确定性量化.

主要方法:

  • 使用塔克分解的贝叶斯张量对张量回归.
  • 为后置推理开发马尔科夫链蒙特卡洛 (MCMC) 算法.
  • 一个基于优化的算法,用于维度优化的模拟化.

主要成果:

  • 拟议的贝叶斯框架使模型维度和参数的联合估计成为可能.
  • 广泛的模拟显示出比其他方法更优越的性能.
  • 该方法在面部成像和3D运动数据上显示了实际的有效性.

结论:

  • 开发的贝叶斯张量对张量回归为多维数据预测提供了一个强大的工具.
  • 同时估计尺寸和参数解决了现有方法的一个关键局限性.
  • 该方法提供了可靠的不确定性量化和实际适用性.