詹森-香农分歧与最小分歧之间的紧密界限
Arseniy Akopyan1, Herbert Edelsbrunner2, Žiga Virk3,4
1Fora Capital, Miami, FL 33131, USA.
Entropy (Basel, Switzerland)
|August 28, 2025
概括
我们表明最小分歧,一个复杂的分类分布的测量,可以靠近詹森-香农分歧. 这一发现简化了信息几何学的分析.
科学领域:
- 信息理论
- 几何分析
- 概率与统计学
背景情况:
- 在各种科学领域比较概率分布至关重要.
- 詹森-香农分歧 (JSD) 是一个广泛使用的,可计算的分布比较度量.
- 最小最大分歧提供了一个具有潜在几何解释的替代措施,但在计算上具有挑战性.
研究的目的:
- 为了比较Jensen-Shannon分歧和有限的分类分布的最小分歧.
- 建立这两个不相似度之间的理论联系.
- 调查最小最大分歧的度量属性.
主要方法:
- 使用库尔巴克-莱布勒分歧作为两项措施的基础.
- 开发理论边界以使用詹森-香农分歧来近似最小分歧.
- 分析最小偏差的平方根的特征.
主要成果:
- 最小的分歧可以通过詹森-香农分歧接近.
- 导出边界支持最小偏差的平方根是一个度量.
- 证明了最小偏差的平方根在一个维的情况下是指数.
结论:
- 詹森-香农分歧为计算密集的最小分歧提供了一个实用且准确的近似值.
- 这项研究促进了对信息几何学不相似度的理解.
- 需要进一步的研究来证明一般情况下的minmax分歧的度量属性.
相关概念视频
Divergence and Stokes' Theorems
1.9K
The divergence and Stokes' theorems are a variation of Green's theorem in a higher dimension. They are also a generalization of the fundamental theorem of calculus. The divergence theorem and Stokes' theorem are in a way similar to each other; The divergence theorem relates to the dot product of a vector, while Stokes' theorem relates to the curl of a vector. Many applications in physics and engineering make use of the divergence and Stokes' theorems, enabling us to write...
1.9K
Mean Absolute Deviation
2.7K
The mean absolute deviation is also a measure of the variability of data in a sample. It is the absolute value of the average difference between the data values and the mean.
Let us consider a dataset containing the number of unsold cupcakes in five shops: 10, 15, 8, 7, and 10. Initially, calculate the sample mean. Then calculate the deviation, or the difference, between each data value and the mean. Next, the absolute values of these deviations are added and divided by the sample size to...
Let us consider a dataset containing the number of unsold cupcakes in five shops: 10, 15, 8, 7, and 10. Initially, calculate the sample mean. Then calculate the deviation, or the difference, between each data value and the mean. Next, the absolute values of these deviations are added and divided by the sample size to...
2.7K
Divergence and Curl
1.9K
The divergence of a vector field at a point is the net outward flow of the flux out of a small volume through a closed surface enclosing the volume, as the volume tends to zero. More practically, divergence measures how much a vector field spreads out or diverges from a given point. For an outgoing flux, conventionally, the divergence is positive. The diverging point is often called the "source" of the field. Meanwhile, the negative divergence of a vector field at a point means that the...
1.9K
Routh-Hurwitz Criterion II
400
In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
400
Central Limit Theorem
15.9K
The central limit theorem, abbreviated as clt, is one of the most powerful and useful ideas in all of statistics. The central limit theorem for sample means says that if you repeatedly draw samples of a given size and calculate their means, and create a histogram of those means, then the resulting histogram will tend to have an approximate normal bell shape. In other words, as sample sizes increase, the distribution of means follows the normal distribution more closely.
The sample size, n, that...
The sample size, n, that...
15.9K
Range Rule of Thumb to Interpret Standard Deviation
9.3K
The range rule of thumb in statistics helps us calculate a dataset's minimum and maximum values with known standard deviation. This rule is based on the concept that 95% of all values in a dataset lie within two standard deviations from the mean.
For instance, the range rule of thumb can be used to find the tallest and the shortest student in a class, given the mean student height and standard deviation. If the mean student height is 1.6 m and the standard deviation, s is 0.05 m, the height...
For instance, the range rule of thumb can be used to find the tallest and the shortest student in a class, given the mean student height and standard deviation. If the mean student height is 1.6 m and the standard deviation, s is 0.05 m, the height...
9.3K


