“互信息”的版本间的差异

2020年5月7日 (四) 16:21的版本

此词条暂由彩云小译翻译，未经人工整理和审校，带来阅读不便，请见谅。模板:Information theory

Venn diagram showing additive and subtractive relationships various information measures associated with correlated variables [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math]. The area contained by both circles is the joint entropy [math]\displaystyle{ \Eta(X,Y) }[/math]. The circle on the left (red and violet) is the individual entropy [math]\displaystyle{ \Eta(X) }[/math], with the red being the conditional entropy [math]\displaystyle{ \Eta(X|Y) }[/math]. The circle on the right (blue and violet) is [math]\displaystyle{ \Eta(Y) }[/math], with the blue being [math]\displaystyle{ \Eta(Y|X) }[/math]. The violet is the mutual information [math]\displaystyle{ \operatorname{I}(X;Y) }[/math].

[[Venn diagram showing additive and subtractive relationships various information measures associated with correlated variables [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math]. The area contained by both circles is the joint entropy [math]\displaystyle{ \Eta(X,Y) }[/math]. The circle on the left (red and violet) is the individual entropy [math]\displaystyle{ \Eta(X) }[/math], with the red being the conditional entropy [math]\displaystyle{ \Eta(X|Y) }[/math]. The circle on the right (blue and violet) is [math]\displaystyle{ \Eta(Y) }[/math], with the blue being [math]\displaystyle{ \Eta(Y|X) }[/math]. The violet is the mutual information [math]\displaystyle{ \operatorname{I}(X;Y) }[/math].]]

显示加减关系的文氏图各种信息测量与相关变量数学 x / 数学和 y / 数学相关。两个圆所包含的面积是联合熵 math Eta (x，y) / math。左边的圆圈(红色和紫色)是个体熵数学 Eta (x) / math，红色的是条件熵数学 Eta (x | y) / math。右边的圆(蓝色和紫色)是 math Eta (y) / math，蓝色的是 math Eta (y | x) / math。紫色是互信息 math operatorname { i }(x; y) / math. ]

In probability theory and information theory, the mutual information (MI) of two random variables is a measure of the mutual dependence between the two variables. More specifically, it quantifies the "amount of information" (in units such as shannons, commonly called bits) obtained about one random variable through observing the other random variable. The concept of mutual information is intricately linked to that of entropy of a random variable, a fundamental notion in information theory that quantifies the expected "amount of information" held in a random variable.

In probability theory and information theory, the mutual information (MI) of two random variables is a measure of the mutual dependence between the two variables. More specifically, it quantifies the "amount of information" (in units such as shannons, commonly called bits) obtained about one random variable through observing the other random variable. The concept of mutual information is intricately linked to that of entropy of a random variable, a fundamental notion in information theory that quantifies the expected "amount of information" held in a random variable.

在概率论和信息论中，两个随机变量的互信息是两个变量相互依赖的度量。更具体地说，它量化了通过观察其他随机变量获得的关于一个随机变量的“信息量”(以单位为单位，如 shannons，通常称为位)。互信息的概念与随机变量的熵的概念有着错综复杂的联系，熵是信息论中的一个基本概念，它量化了一个随机变量中预期的“信息量”。

Not limited to real-valued random variables and linear dependence like the correlation coefficient, MI is more general and determines how different the joint distribution of the pair [math]\displaystyle{ (X,Y) }[/math] is to the product of the marginal distributions of [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math]. MI is the expected value of the pointwise mutual information (PMI).

不仅限于实值随机变量和相关系数之类的线性依赖，MI 更为普遍，它决定了一对 math (x，y) / math 的联合分布与 math x / math 和 math y / math 的边际分布的乘积有多大的不同。Mi 是点间互信息指数的预期值。

Mutual Information is also known as information gain.

互信息也称为信息增益。

Definition

定义

Let [math]\displaystyle{ (X,Y) }[/math] be a pair of random variables with values over the space [math]\displaystyle{ \mathcal{X}\times\mathcal{Y} }[/math]. If their joint distribution is [math]\displaystyle{ P_{(X,Y)} }[/math] and the marginal distributions are [math]\displaystyle{ P_X }[/math] and [math]\displaystyle{ P_Y }[/math], the mutual information is defined as

设 math (x，y) / math 是一对值超过空间 math mathcal { x } times mathcal { y } / math 的随机变量。如果它们的联合分布是数学 p {(x，y)} / math，边际分布是数学 p x / math 和数学 p y / math，则互信息定义为

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[math]\displaystyle{ \lt math\gt 数学 I(X;Y) = D_{\mathrm{KL}}( P_{(X,Y)} \| P_{X} \otimes P_{Y} ) I(X;Y) = D_{\mathrm{KL}}( P_{(X,Y)} \| P_{X} \otimes P_{Y} ) I (x; y) d { mathrm { KL }(p {(x，y)} | p { x }乘 p { y }) }[/math]

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where [math]\displaystyle{ D_{\mathrm{KL}} }[/math] is the Kullback–Leibler divergence.

其中数学 d (mathrm { KL } / math)是 Kullback-Leibler 分歧。

Notice, as per property of the Kullback–Leibler divergence, that [math]\displaystyle{ I(X;Y) }[/math] is equal to zero precisely when the joint distribution coincides with the product of the marginals, i.e. when [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math] are independent (and hence observing [math]\displaystyle{ Y }[/math] tells your nothing about [math]\displaystyle{ X }[/math]). In general [math]\displaystyle{ I(X;Y) }[/math] is non-negative, it is a measure of the price for encoding [math]\displaystyle{ (X,Y) }[/math] as a pair of independent random variables, when in reality they are not.

注意，根据 Kullback-Leibler 散度的性质，当联合分布与边际乘积重合时，math i (x; y) / math 恰好等于零，即。当数学 x / 数学和数学 y / 数学是独立的(因此观察数学 y / 数学并不能告诉你数学 x / 数学)。在一般的数学 i (x; y) / math 是非负的，它是一种测量方法，用于将 math (x，y) / math 作为一对独立的随机变量进行编码，而实际上它们并不是。

In terms of PMFs for discrete distributions

根据离散分布的保偏光纤

The mutual information of two jointly discrete random variables [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math] is calculated as a double sum:^[1]^:20

The mutual information of two jointly discrete random variables [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math] is calculated as a double sum:

两个联合离散随机变量的互信息数学 x / math 和数学 y / math 被计算为一个双和:

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[math]\displaystyle{ |equation = {{NumBlk||\lt math\gt 这个问题的答案是: \operatorname{I}(X;Y) = \sum_{y \in \mathcal Y} \sum_{x \in \mathcal X} \operatorname{I}(X;Y) = \sum_{y \in \mathcal Y} \sum_{x \in \mathcal X} 运算符名称{ i }(x; y) sum { y } sum { x } { p_{(X,Y)}(x,y) \log{ \left(\frac{p_{(X,Y)}(x,y)}{p_X(x)\,p_Y(y)} \right) }}, { p_{(X,Y)}(x,y) \log{ \left(\frac{p_{(X,Y)}(x,y)}{p_X(x)\,p_Y(y)} \right) }}, { p {(x，y)}(x，y) log { left ( frac { p {(x，y)}(x，y)}{ p x (x) ，p y (y)}右)} , }[/math]

(Eq.1)

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where [math]\displaystyle{ p_{(X,Y)} }[/math] is the joint probability mass function of [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math], and [math]\displaystyle{ p_X }[/math] and [math]\displaystyle{ p_Y }[/math] are the marginal probability mass functions of [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math] respectively.

where [math]\displaystyle{ p_{(X,Y)} }[/math] is the joint probability mass function of [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math], and [math]\displaystyle{ p_X }[/math] and [math]\displaystyle{ p_Y }[/math] are the marginal probability mass functions of [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math] respectively.

数学 p {(x，y)} / math 是数学 x / math 和数学 y / math 的概率质量函数，而数学 p x / math 和数学 p y / math 分别是数学 x / math 和数学 y / math 的边际概率质量函数。

In terms of PDFs for continuous distributions

在连续分布的 pdf 方面

In the case of jointly continuous random variables, the double sum is replaced by a double integral:^[1]^:251

In the case of jointly continuous random variables, the double sum is replaced by a double integral:

在联合连续随机变量的情况下，二重和用二重积分代替:

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[math]\displaystyle{ |equation = {{NumBlk||\lt math\gt 这个问题的答案是: \operatorname{I}(X;Y) = \operatorname{I}(X;Y) = 操作者名{ i }(x; y) \int_{\mathcal Y} \int_{\mathcal X} \int_{\mathcal Y} \int_{\mathcal X} 从数学的角度出发，提出了一种新的数学分析方法 {p_{(X,Y)}(x,y) \log{ \left(\frac{p_{(X,Y)}(x,y)}{p_X(x)\,p_Y(y)} \right) } {p_{(X,Y)}(x,y) \log{ \left(\frac{p_{(X,Y)}(x,y)}{p_X(x)\,p_Y(y)} \right) } { p {(x，y)}(x，y) log { p {(x，y)}(x，y)}{ p x (x) ，p y (y)}右)} } \; dx \,dy, } \; dx \,dy, 开始吧，迪, }[/math]

(Eq.2)

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where [math]\displaystyle{ p_{(X,Y)} }[/math] is now the joint probability density function of [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math], and [math]\displaystyle{ p_X }[/math] and [math]\displaystyle{ p_Y }[/math] are the marginal probability density functions of [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math] respectively.

where [math]\displaystyle{ p_{(X,Y)} }[/math] is now the joint probability density function of [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math], and [math]\displaystyle{ p_X }[/math] and [math]\displaystyle{ p_Y }[/math] are the marginal probability density functions of [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math] respectively.

数学 p {(x，y)} / math 现在是数学 x / math 和数学 y / math 的联合概率密度函数，而数学 p x / math 和数学 p y / math 分别是数学 x / math 和数学 y / math 的边际概率密度函数。

If the log base 2 is used, the units of mutual information are bits.

如果使用日志基数2，则互信息的单位为位。

Motivation

动机

Intuitively, mutual information measures the information that [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math] share: It measures how much knowing one of these variables reduces uncertainty about the other. For example, if [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math] are independent, then knowing [math]\displaystyle{ X }[/math] does not give any information about [math]\displaystyle{ Y }[/math] and vice versa, so their mutual information is zero. At the other extreme, if [math]\displaystyle{ X }[/math] is a deterministic function of [math]\displaystyle{ Y }[/math] and [math]\displaystyle{ Y }[/math] is a deterministic function of [math]\displaystyle{ X }[/math] then all information conveyed by [math]\displaystyle{ X }[/math] is shared with [math]\displaystyle{ Y }[/math]: knowing [math]\displaystyle{ X }[/math] determines the value of [math]\displaystyle{ Y }[/math] and vice versa. As a result, in this case the mutual information is the same as the uncertainty contained in [math]\displaystyle{ Y }[/math] (or [math]\displaystyle{ X }[/math]) alone, namely the entropy of [math]\displaystyle{ Y }[/math] (or [math]\displaystyle{ X }[/math]). Moreover, this mutual information is the same as the entropy of [math]\displaystyle{ X }[/math] and as the entropy of [math]\displaystyle{ Y }[/math]. (A very special case of this is when [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math] are the same random variable.)

直观地说，互信息测量了 math x / math 和 math y / math 共享的信息: 它测量了解其中一个变量减少了另一个变量的不确定性的程度。例如，如果数学 x / math 和数学 y / math 是独立的，那么知道数学 x / math 并不会给出任何关于数学 y / math 的信息，反之亦然，因此它们的相互信息为零。在另一个极端，如果数学 x / math 是数学 y / math 的确定性函数，而数学 y / math 是数学 x / math 的确定性函数，那么数学 x / math 传递的所有信息都与数学 y / math 共享: 知道数学 x / math 决定数学 y / math 的值，反之亦然。因此，在这种情况下，互信息与数学 y / math (或者数学 x / math)中包含的不确定性是一样的，即数学 y / math (或者数学 x / math)的熵。此外，这种互信息与数学 x / 数学的熵和数学 y / 数学的熵是一样的。(一个非常特殊的例子是，当数学 x / math 和数学 y / math 是同一个随机变量时。)

Mutual information is a measure of the inherent dependence expressed in the joint distribution of [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math] relative to the joint distribution of [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math] under the assumption of independence. Mutual information therefore measures dependence in the following sense: [math]\displaystyle{ \operatorname{I}(X;Y)=0 }[/math] if and only if [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math] are independent random variables. This is easy to see in one direction: if [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math] are independent, then [math]\displaystyle{ p_{(X,Y)}(x,y)=p_X(x) \cdot p_Y(y) }[/math], and therefore:

互信息是数学 x / 数学 y / 数学关于数学 x / 数学 y / 数学关于数学 x / 数学 y / 数学关于数学 x / 数学关于数学 y / 数学关于数学 x / 数学关于数学 x / 数学关于数学 y / 数学关于数学关于。因此，互信息在以下意义上度量依赖性: math operatorname { i }(x; y)0 / math 当且仅当 math x / math 和 math y / math 是独立的随机变量。这很容易从一个方向看出来: 如果数学 x / math 和数学 y / math 是独立的，那么数学 p {(x，y)}(x，y) p x (x) cdot py (y) / math，因此:

[math]\displaystyle{ \log{ \left( \frac{p_{(X,Y)}(x,y)}{p_X(x)\,p_Y(y)} \right) } = \log 1 = 0 . }[/math]

(x，y)}{ p x (x) ，p y (y)} log 10. / math

Moreover, mutual information is nonnegative (i.e. [math]\displaystyle{ \operatorname{I}(X;Y) \ge 0 }[/math] see below) and symmetric (i.e. [math]\displaystyle{ \operatorname{I}(X;Y) = \operatorname{I}(Y;X) }[/math] see below).

此外，互信息是非负的(即。(x; y) ge 0 / math 见下文)和对称(即。Math operatorname { i }(x; y) operatorname { i }(y; x) / math 见下文)。

Relation to other quantities

与其他量的关系

Nonnegativity

非消极性

Using Jensen's inequality on the definition of mutual information we can show that [math]\displaystyle{ \operatorname{I}(X;Y) }[/math] is non-negative, i.e.^[1]^:28

Using Jensen's inequality on the definition of mutual information we can show that [math]\displaystyle{ \operatorname{I}(X;Y) }[/math] is non-negative, i.e.

利用 Jensen 不等式对互信息的定义，我们可以证明 math operatorname { i }(x; y) / math 是非负的，即。

[math]\displaystyle{ \operatorname{I}(X;Y) \ge 0 }[/math]

{ i }(x; y) ge 0 / math

Symmetry

对称性

[math]\displaystyle{ \operatorname{I}(X;Y) = \operatorname{I}(Y;X) }[/math]

Math operatorname { i }(x; y) operatorname { i }(y; x) / math

Relation to conditional and joint entropy

条件熵与联合熵的关系

Mutual information can be equivalently expressed as:

互信息可以等价地表示为:

[math]\displaystyle{ \begin{align} \lt math\gt \begin{align} 数学 begin { align } \operatorname{I}(X;Y) &{} \equiv \Eta(X) - \Eta(X|Y) \\ \operatorname{I}(X;Y) &{} \equiv \Eta(X) - \Eta(X|Y) \\ 运营商名称{ i }(x; y) & { equiv Eta (x)- Eta (x | y) &{} \equiv \Eta(Y) - \Eta(Y|X) \\ &{} \equiv \Eta(Y) - \Eta(Y|X) \\ 埃塔(y)-埃塔(y | x) &{} \equiv \Eta(X) + \Eta(Y) - \Eta(X, Y) \\ &{} \equiv \Eta(X) + \Eta(Y) - \Eta(X, Y) \\ (x) + Eta (y)-Eta (x，y) &{} \equiv \Eta(X, Y) - \Eta(X|Y) - \Eta(Y|X) &{} \equiv \Eta(X, Y) - \Eta(X|Y) - \Eta(Y|X) (x，y)-Eta (x | y)-Eta (y | x) \end{align} }[/math]

\end{align}</math>

End { align } / math

where [math]\displaystyle{ \Eta(X) }[/math] and [math]\displaystyle{ \Eta(Y) }[/math] are the marginal entropies, [math]\displaystyle{ \Eta(X|Y) }[/math] and [math]\displaystyle{ \Eta(Y|X) }[/math] are the conditional entropies, and [math]\displaystyle{ \Eta(X,Y) }[/math] is the joint entropy of [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math].

其中 math Eta (x) / math Eta (y) / math 是边际熵，math Eta (x | y) / math Eta (y | x) / math Eta (y | x) / math 是条件熵，math Eta (x，y) / math 是数学 x / math 和数学 y / math 的联合熵。

Notice the analogy to the union, difference, and intersection of two sets: in this respect, all the formulas given above are apparent from the Venn diagram reported at the beginning of the article.

注意对两个集合的并、差和交集的类比: 在这方面，上面给出的所有公式在文章开头报告的维恩图中都是显而易见的。

In terms of a communication channel in which the output [math]\displaystyle{ Y }[/math] is a noisy version of the input [math]\displaystyle{ X }[/math], these relations are summarised in the figure:

在通信通道中，输出数学 y / math 是输入数学 x / math 的噪声版本，这些关系如图所示:

文件:Figchannel2017ab.svg

The relationships between information theoretic quantities

The relationships between information theoretic quantities

信息理论量之间的关系

Because [math]\displaystyle{ \operatorname{I}(X;Y) }[/math] is non-negative, consequently, [math]\displaystyle{ \Eta(X) \ge \Eta(X|Y) }[/math]. Here we give the detailed deduction of [math]\displaystyle{ \operatorname{I}(X;Y)=\Eta(Y)-\Eta(Y|X) }[/math] for the case of jointly discrete random variables:

因为 math operatorname { i }(x; y) / math 是非负的，所以 math Eta (x) ge Eta (x | y) / math。本文给出了联合离散随机变量的 math operatorname { i }(x; y) Eta (y)- Eta (y | x) / math 的详细推导:

[math]\displaystyle{ \lt math\gt 数学 \begin{align} \begin{align} Begin { align } \operatorname{I}(X;Y) & {} = \sum_{x \in \mathcal{X},y \in \mathcal{Y}} p_{(X,Y)}(x,y) \log \frac{p_{(X,Y)}(x,y)}{p_X(x)p_Y(y)}\\ \operatorname{I}(X;Y) & {} = \sum_{x \in \mathcal{X},y \in \mathcal{Y}} p_{(X,Y)}(x,y) \log \frac{p_{(X,Y)}(x,y)}{p_X(x)p_Y(y)}\\ 运算符名称{ i }(x; y) & { sum { in mathcal { x，y } p {(x，y)}(x，y) log frac { p (x，y)}(x，y)}{ p x (x) p (x) y (y)} & {} = \sum_{x \in \mathcal{X},y \in \mathcal{Y}} p_{(X,Y)}(x,y) \log \frac{p_{(X,Y)}(x,y)}{p_X(x)} - \sum_{x \in \mathcal{X},y \in \mathcal{Y}} p_{(X,Y)}(x,y) \log p_Y(y) \\ & {} = \sum_{x \in \mathcal{X},y \in \mathcal{Y}} p_{(X,Y)}(x,y) \log \frac{p_{(X,Y)}(x,y)}{p_X(x)} - \sum_{x \in \mathcal{X},y \in \mathcal{Y}} p_{(X,Y)}(x,y) \log p_Y(y) \\ (x，y){ x，y)}(x，y) log frac { p (x，y)}(x，y)}{ x (x)}-sum { x (x)}-{{ y }{ mathcal {(x，y)}(x，y)}(y) log py (y)} & {} = \sum_{x \in \mathcal{X},y \in \mathcal{Y}} p_X(x)p_{Y|X=x}(y) \log p_{Y|X=x}(y) - \sum_{x \in \mathcal{X},y \in \mathcal{Y}} p_{(X,Y)}(x,y) \log p_Y(y) \\ & {} = \sum_{x \in \mathcal{X},y \in \mathcal{Y}} p_X(x)p_{Y|X=x}(y) \log p_{Y|X=x}(y) - \sum_{x \in \mathcal{X},y \in \mathcal{Y}} p_{(X,Y)}(x,y) \log p_Y(y) \\ 数学 x，y 中数学 y 中数学 x (x) p { y | x }(y) log p { y | x }(y)-数学 x 中数学 y 中数学 y 中数学 y 中数学 y 中数学 y 中数学 y 中数学 y 中数学 y 中数学 y 中数学 y 中数学 y 中数学 y 中数学 y 中数学 y 中数学 y 中数学 y 中数学 y 中数学 y 中数学 y 中数学 y 中数学 y 中数学 y 中数学 y 中数学 y 中数学 y 中数学 y 中数学 y 中数学 y 中数学 y 中的数学 y & {} = \sum_{x \in \mathcal{X}} p_X(x) \left(\sum_{y \in \mathcal{Y}} p_{Y|X=x}(y) \log p_{Y|X=x}(y)\right) - \sum_{y \in \mathcal{Y}} \left(\sum_x p_{(X,Y)}(x,y)\right) \log p_Y(y) \\ & {} = \sum_{x \in \mathcal{X}} p_X(x) \left(\sum_{y \in \mathcal{Y}} p_{Y|X=x}(y) \log p_{Y|X=x}(y)\right) - \sum_{y \in \mathcal{Y}} \left(\sum_x p_{(X,Y)}(x,y)\right) \log p_Y(y) \\ 数学 x (x)左(和 y) n 数学 y | x }(y) log p { y | x }(y)右)-数学 y 左(和 x (x，y)右) log p y (y) & {} = -\sum_{x \in \mathcal{X}} p(x) \Eta(Y|X=x) - \sum_{y \in \mathcal{Y}} p_Y(y) \log p_Y(y) \\ & {} = -\sum_{x \in \mathcal{X}} p(x) \Eta(Y|X=x) - \sum_{y \in \mathcal{Y}} p_Y(y) \log p_Y(y) \\ 数学中的和(x) p (x) Eta (y | x)-和(y) y (y) log py (y) & {} = -\Eta(Y|X) + \Eta(Y) \\ & {} = -\Eta(Y|X) + \Eta(Y) \\ {}- Eta (y | x) + Eta (y) & {} = \Eta(Y) - \Eta(Y|X). \\ & {} = \Eta(Y) - \Eta(Y|X). \\ 埃塔(y)-埃塔(y | x)。\\ \end{align} \end{align} End { align } }[/math]

</math>

数学

The proofs of the other identities above are similar. The proof of the general case (not just discrete) is similar, with integrals replacing sums.

上述其他恒等式的证明是相似的。一般情况(不仅仅是离散情况)的证明是类似的，用积分代替和。

Intuitively, if entropy [math]\displaystyle{ \Eta(Y) }[/math] is regarded as a measure of uncertainty about a random variable, then [math]\displaystyle{ \Eta(Y|X) }[/math] is a measure of what [math]\displaystyle{ X }[/math] does not say about [math]\displaystyle{ Y }[/math]. This is "the amount of uncertainty remaining about [math]\displaystyle{ Y }[/math] after [math]\displaystyle{ X }[/math] is known", and thus the right side of the second of these equalities can be read as "the amount of uncertainty in [math]\displaystyle{ Y }[/math], minus the amount of uncertainty in [math]\displaystyle{ Y }[/math] which remains after [math]\displaystyle{ X }[/math] is known", which is equivalent to "the amount of uncertainty in [math]\displaystyle{ Y }[/math] which is removed by knowing [math]\displaystyle{ X }[/math]". This corroborates the intuitive meaning of mutual information as the amount of information (that is, reduction in uncertainty) that knowing either variable provides about the other.

Intuitively, if entropy [math]\displaystyle{ \Eta(Y) }[/math] is regarded as a measure of uncertainty about a random variable, then [math]\displaystyle{ \Eta(Y|X) }[/math] is a measure of what [math]\displaystyle{ X }[/math] does not say about [math]\displaystyle{ Y }[/math]. This is "the amount of uncertainty remaining about [math]\displaystyle{ Y }[/math] after [math]\displaystyle{ X }[/math] is known", and thus the right side of the second of these equalities can be read as "the amount of uncertainty in [math]\displaystyle{ Y }[/math], minus the amount of uncertainty in [math]\displaystyle{ Y }[/math] which remains after [math]\displaystyle{ X }[/math] is known", which is equivalent to "the amount of uncertainty in [math]\displaystyle{ Y }[/math] which is removed by knowing [math]\displaystyle{ X }[/math]". This corroborates the intuitive meaning of mutual information as the amount of information (that is, reduction in uncertainty) that knowing either variable provides about the other.

直观地说，如果熵的数学 Eta (y) / 数学被看作是对一个随机变量的不确定性的度量，那么 math (y | x) / math 是对数学 x / math 没有对数学 y / math 进行说明的度量。这是“数学 x / 数学已知后数学 y / 数学剩余的不确定性量” ，因此，第二个等式的右边可以被解读为“数学 y / 数学中的不确定性量，减去数学 y / 数学中的不确定性量，在数学 x / 数学已知后仍然存在的不确定性量” ，这相当于“数学 y / 数学中的不确定性量，通过知道数学 x / 数学而去除”。这证实了互信息的直观含义，即知道任何一个变量提供的关于另一个变量的信息量(即不确定性的减少)。

Note that in the discrete case [math]\displaystyle{ \Eta(X|X) = 0 }[/math] and therefore [math]\displaystyle{ \Eta(X) = \operatorname{I}(X;X) }[/math]. Thus [math]\displaystyle{ \operatorname{I}(X; X) \ge \operatorname{I}(X; Y) }[/math], and one can formulate the basic principle that a variable contains at least as much information about itself as any other variable can provide.

注意，在离散情况下 math Eta (x | x)0 / math，因此 math Eta (x) operatorname { i }(x; x) / math。因此 math operatorname { i }(x; x) ge operatorname { i }(x; y) / math，我们可以公式化这样一个基本原则，即一个变量包含的关于它自身的信息至少与任何其他变量所能提供的信息一样多。

Relation to Kullback–Leibler divergence

与 Kullback-莱布勒分歧的关系

For jointly discrete or jointly continuous pairs [math]\displaystyle{ (X,Y) }[/math],

对于联合离散或联合连续对数学(x，y) / 数学,

mutual information is the Kullback–Leibler divergence of the product of the marginal distributions, [math]\displaystyle{ p_X \cdot p_Y }[/math], from the joint distribution [math]\displaystyle{ p_{(X,Y)} }[/math], that is,

互信息是边际分布乘积的 Kullback-Leibler 散度，也就是联合分布数学 p {(x，y)} / math 的乘积,

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[math]\displaystyle{ \lt math\gt 数学 \operatorname{I}(X; Y) = D_\text{KL}\left(p_{(X,Y)} \parallel p_Xp_Y\right) \operatorname{I}(X; Y) = D_\text{KL}\left(p_{(X,Y)} \parallel p_Xp_Y\right) 操作数名{ i }(x; y) d text { KL }左(p {(x，y)}并行 p xp y 右) }[/math]

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Furthermore, let [math]\displaystyle{ p_{X|Y=y}(x) = p_{(X,Y)}(x,y) / p_Y(y) }[/math] be the conditional mass or density function. Then, we have the identity

进一步，设 p { x | y }(x) p {(x，y)}(x，y) / p y (y) / math 是条件质量或密度函数。那么，我们就有了身份

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[math]\displaystyle{ \lt math\gt 数学 \operatorname{I}(X;Y) = \mathbb{E}_Y\left[D_\text{KL}\!\left(p_{X|Y} \parallel p_X\right)\right] \operatorname{I}(X;Y) = \mathbb{E}_Y\left[D_\text{KL}\!\left(p_{X|Y} \parallel p_X\right)\right] [运算符名称{ i }(x; y) mathbb { e } y 左[ d text { KL } ! 左(p { x | y }并行 p 右)右]] }[/math]

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数学

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The proof for jointly discrete random variables is as follows:

联合离散随机变量的证明如下:

[math]\displaystyle{ \lt math\gt 数学 \begin{align} \begin{align} Begin { align } \operatorname{I}(X;Y) &= \sum_{y \in \mathcal{Y}} p_Y(y) \sum_{x \in \mathcal{X}} p_{X|Y=y}(x) \log \frac{p_{X|Y=y}(x)}{p_X(x)} \\ \operatorname{I}(X;Y) &= \sum_{y \in \mathcal{Y}} p_Y(y) \sum_{x \in \mathcal{X}} p_{X|Y=y}(x) \log \frac{p_{X|Y=y}(x)}{p_X(x)} \\ 运算符名称{ i }(x; y) & sum { y } p (y) sum { x } p { x | y }(x) log { y }(x)}{ p (x)}} &= \sum_{y \in \mathcal{Y}} p_Y(y) \; D_\text{KL}\!\left(p_{X|Y=y} \parallel p_X\right) \\ &= \sum_{y \in \mathcal{Y}} p_Y(y) \; D_\text{KL}\!\left(p_{X|Y=y} \parallel p_X\right) \\ 数学上的 y } p y (y) ; d text { KL } ! 左(p { x | y }并行 p 右) &= \mathbb{E}_Y \left[D_\text{KL}\!\left(p_{X|Y} \parallel p_X\right)\right]. &= \mathbb{E}_Y \left[D_\text{KL}\!\left(p_{X|Y} \parallel p_X\right)\right]. & mathbb { e } y 左[ d text { KL } ! 左(p { x | y }并行 p 右)右]。 \end{align} \end{align} End { align } }[/math]

</math>

数学

Similarly this identity can be established for jointly continuous random variables.

同样，这个恒等式也可以建立在联合连续的随机变量上。

Note that here the Kullback–Leibler divergence involves integration over the values of the random variable [math]\displaystyle{ X }[/math] only, and the expression [math]\displaystyle{ D_\text{KL}(p_{X|Y} \parallel p_X) }[/math] still denotes a random variable because [math]\displaystyle{ Y }[/math] is random. Thus mutual information can also be understood as the expectation of the Kullback–Leibler divergence of the univariate distribution [math]\displaystyle{ p_X }[/math] of [math]\displaystyle{ X }[/math] from the conditional distribution [math]\displaystyle{ p_{X|Y} }[/math] of [math]\displaystyle{ X }[/math] given [math]\displaystyle{ Y }[/math]: the more different the distributions [math]\displaystyle{ p_{X|Y} }[/math] and [math]\displaystyle{ p_X }[/math] are on average, the greater the information gain.

注意，这里 Kullback-Leibler 散度只涉及对随机变量 math x / math 的值的积分，而 math d text { KL }(p { x | y } parallel px) / math 表达式仍然表示随机变量，因为 math y / math 是随机的。因此，互信息也可以理解为单变量分布数学公式的 Kullback-Leibler 散度与条件分布数学公式 p { x | y } / 数学公式的数学公式 p { x | y } / 数学公式给定数学公式 y / 数学公式的数学公式的期望值: 平均分布公式 p { x | y } / 数学公式 p x / 数学公式 p x / 数学公式的分布越不相同，信息增益越大。

Bayesian estimation of mutual information

互信息的贝叶斯估计

It is well-understood how to do Bayesian estimation of the mutual information

如何进行互信息的贝叶斯估计是一个众所周知的问题

of a joint distribution based on samples of that distribution. The

联合分布的基础上，该分布的样本。这个

first work to do this, which also showed how to do Bayesian estimation of many

第一个工作这样做，这也显示了如何做贝叶斯估计的许多

other information-theoretic properties besides mutual information, was ^[2]. Subsequent researchers have rederived ^[3]

other information-theoretic properties besides mutual information, was . Subsequent researchers have rederived

除了互信息之外，还有其他信息论性质。后来的研究人员重新推导出

and extended ^[4]

and extended

还有延伸

this analysis. See ^[5]

this analysis. See

这个分析。看

for a recent paper based on a prior specifically tailored to estimation of mutual

在最近的一篇论文中，基于之前专门针对相互性的评估

information per se. Besides, recently an estimation method accounting for continuous and multivariate outputs, [math]\displaystyle{ Y }[/math], was proposed in

信息本身。此外，最近提出了一种考虑连续和多变量输出的估计方法，即数学 y / math

^[6].

.

Independence assumptions

独立性假设

The Kullback-Leibler divergence formulation of the mutual information is predicated on that one is interested in comparing [math]\displaystyle{ p(x,y) }[/math] to the fully factorized outer product [math]\displaystyle{ p(x) \cdot p(y) }[/math]. In many problems, such as non-negative matrix factorization, one is interested in less extreme factorizations; specifically, one wishes to compare [math]\displaystyle{ p(x,y) }[/math] to a low-rank matrix approximation in some unknown variable [math]\displaystyle{ w }[/math]; that is, to what degree one might have

互信息的 Kullback-Leibler 散度公式是基于人们对数学 p (x，y) / math 与完全分解的外积 math p (x) cdot p (y) / math 的比较感兴趣。在许多问题中，比如非负矩阵分解，人们对非极端的因式分解感兴趣; 具体地说，人们希望将数学 p (x，y) / math 与某个未知变量 math w / math 中的低秩矩阵近似值进行比较; 也就是说，人们可能具有的程度

[math]\displaystyle{ p(x,y)\approx \sum_w p^\prime (x,w) p^{\prime\prime}(w,y) }[/math]

约和 w p ^ prime (x，w) p ^ prime }(w，y) / math

Alternately, one might be interested in knowing how much more information [math]\displaystyle{ p(x,y) }[/math] carries over its factorization. In such a case, the excess information that the full distribution [math]\displaystyle{ p(x,y) }[/math] carries over the matrix factorization is given by the Kullback-Leibler divergence

或者，你可能会有兴趣知道 p (x，y) / math 的因式分解会带来多少信息。在这种情况下，完整的分布数学 p (x，y) / 数学传递给矩阵分解的剩余信息是由 Kullback-Leibler 分歧给出的

[math]\displaystyle{ \operatorname{I}_{LRMA} = \sum_{y \in \mathcal{Y}} \sum_{x \in \mathcal{X}} \lt math\gt \operatorname{I}_{LRMA} = \sum_{y \in \mathcal{Y}} \sum_{x \in \mathcal{X}} 数学运算符名称{ i }{ LRMA } sum { y } in 数学{ y } sum { x } {p(x,y) \log{ \left(\frac{p(x,y)}{\sum_w p^\prime (x,w) p^{\prime\prime}(w,y)} {p(x,y) \log{ \left(\frac{p(x,y)}{\sum_w p^\prime (x,w) p^{\prime\prime}(w,y)} { p (x，y) log {( frac { p (x，y)}{和 w ^ prime (x，w) p ^ prime }(w，y)} \right) }}, \right) }}, 开始，开始, }[/math]

</math>

数学

The conventional definition of the mutual information is recovered in the extreme case that the process [math]\displaystyle{ W }[/math] has only one value for [math]\displaystyle{ w }[/math].

在进程数学 w / math 只有一个数学 w / math 值的极端情况下，恢复了互信息的传统定义。

Variations

变化

Several variations on mutual information have been proposed to suit various needs. Among these are normalized variants and generalizations to more than two variables.

为了适应不同的需要，已经提出了几种互信息的变体。其中包括对两个以上变量的规范化变量和泛化。

Metric

公制

Many applications require a metric, that is, a distance measure between pairs of points. The quantity

许多应用程序需要一个度量单位，即两个点之间的距离度量单位。数量

[math]\displaystyle{ \lt math\gt 数学 \begin{align} \begin{align} Begin { align } d(X,Y) &= \Eta(X,Y) - \operatorname{I}(X;Y) \\ d(X,Y) &= \Eta(X,Y) - \operatorname{I}(X;Y) \\ D (x，y) & Eta (x，y)-操作者名{ i }(x; y) &= \Eta(X) + \Eta(Y) - 2\operatorname{I}(X;Y) \\ &= \Eta(X) + \Eta(Y) - 2\operatorname{I}(X;Y) \\ & Eta (x) + Eta (y)-2运营商名称{ i }(x; y) &= \Eta(X|Y) + \Eta(Y|X) &= \Eta(X|Y) + \Eta(Y|X) & Eta (x | y) + Eta (y | x) \end{align} \end{align} End { align } }[/math]

</math>

数学

satisfies the properties of a metric (triangle inequality, non-negativity, indiscernability and symmetry). This distance metric is also known as the variation of information.

满足度量的性质(三角不等式、非负性、不可分性和对称性)。这个距离度量也称为信息的变化。

If [math]\displaystyle{ X, Y }[/math] are discrete random variables then all the entropy terms are non-negative, so [math]\displaystyle{ 0 \le d(X,Y) \le \Eta(X,Y) }[/math] and one can define a normalized distance

如果数学 x，y / math 是离散随机变量，那么所有的熵项都是非负的，所以数学0 le d (x，y) le Eta (x，y) / math 可以定义一个标准化距离

[math]\displaystyle{ D(X,Y) = \frac{d(X, Y)}{\Eta(X, Y)} \le 1. }[/math]

数学 d (x，y) frac { d (x，y)}{ Eta (x，y)} le 1. / math

The metric [math]\displaystyle{ D }[/math] is a universal metric, in that if any other distance measure places [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math] close-by, then the [math]\displaystyle{ D }[/math] will also judge them close.^[7]模板:Dubious

The metric [math]\displaystyle{ D }[/math] is a universal metric, in that if any other distance measure places [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math] close-by, then the [math]\displaystyle{ D }[/math] will also judge them close.

数学公制 d / math 是一个通用的公制，因为如果任何其他距离公制把数学 x / math 和数学 y / math 放在附近，那么数学公制 d / math 也会把它们放在附近。

Plugging in the definitions shows that

插入定义表明

[math]\displaystyle{ D(X,Y) = 1 - \frac{\operatorname{I}(X; Y)}{\Eta(X, Y)}. }[/math]

Math d (x，y)1- frac { operatorname { i }(x; y)}{ Eta (x，y)} . / math

In a set-theoretic interpretation of information (see the figure for Conditional entropy), this is effectively the Jaccard distance between [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math].

在对信息的集合论解释中(见条件熵图) ，这实际上是数学 x / 数学和数学 y / 数学之间的雅可比相似度系数。

Finally,

最后,

[math]\displaystyle{ D^\prime(X, Y) = 1 - \frac{\operatorname{I}(X; Y)}{\max\left\{\Eta(X), \Eta(Y)\right\}} }[/math]

数学 d ^ 素数(x，y)1-frac 算子名{ i }(x; y)}{ max 左 Eta (x) ，Eta (y)右} / math

is also a metric.

也是一个度量标准。

Conditional mutual information

条件互信息

Sometimes it is useful to express the mutual information of two random variables conditioned on a third.

有时表达两个以第三个为条件的随机变量的相互信息是有用的。

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[math]\displaystyle{ \lt math\gt 数学 \operatorname{I}(X;Y|Z) = \mathbb{E}_Z [D_{\mathrm{KL}}( P_{(X,Y)|Z} \| P_{X|Z} \otimes P_{Y|Z} )] \operatorname{I}(X;Y|Z) = \mathbb{E}_Z [D_{\mathrm{KL}}( P_{(X,Y)|Z} \| P_{X|Z} \otimes P_{Y|Z} )] [操作数名{ i }(x; y | z) mathbb { e } z [ d {(x，y) | z } | p { x | z }乘以 p { y | z }] }[/math]

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For jointly discrete random variables this takes the form

对于共同离散的随机变量，这采取的形式

[math]\displaystyle{ \lt math\gt 数学 \operatorname{I}(X;Y|Z) = \sum_{z\in \mathcal{Z}} \sum_{y\in \mathcal{Y}} \sum_{x\in \mathcal{X}} \operatorname{I}(X;Y|Z) = \sum_{z\in \mathcal{Z}} \sum_{y\in \mathcal{Y}} \sum_{x\in \mathcal{X}} 运算符名称{ i }(x; y | z) sum { z } sum { y } in mathcal { y } sum { x } {p_Z(z)\, p_{X,Y|Z}(x,y|z) {p_Z(z)\, p_{X,Y|Z}(x,y|z) { p z (z) ，p { x，y | z }(x，y | z) \log\left[\frac{p_{X,Y|Z}(x,y|z)}{p_{X|Z}\,(x|z)p_{Y|Z}(y|z)}\right]}, \log\left[\frac{p_{X,Y|Z}(x,y|z)}{p_{X|Z}\,(x|z)p_{Y|Z}(y|z)}\right]}, 向左[ frac { x，y | z }(x，y | z)}{ p { x | z } ，(x | z) p { y | z }(y | z)}右]} , }[/math]

</math>

数学

which can be simplified as

可以简化为

[math]\displaystyle{ \lt math\gt 数学 \operatorname{I}(X;Y|Z) = \sum_{z\in \mathcal{Z}} \sum_{y\in \mathcal{Y}} \sum_{x\in \mathcal{X}} \operatorname{I}(X;Y|Z) = \sum_{z\in \mathcal{Z}} \sum_{y\in \mathcal{Y}} \sum_{x\in \mathcal{X}} 运算符名称{ i }(x; y | z) sum { z } sum { y } in mathcal { y } sum { x } p_{X,Y,Z}(x,y,z) \log \frac{p_{X,Y,Z}(x,y,z)p_{Z}(z)}{p_{X,Z}(x,z)p_{Y,Z}(y,z)}. p_{X,Y,Z}(x,y,z) \log \frac{p_{X,Y,Z}(x,y,z)p_{Z}(z)}{p_{X,Z}(x,z)p_{Y,Z}(y,z)}. P { x，y，z }(x，y，z) log frac { x，y，z }(x，y，z) p { z }(z)}(x，z) p { y，z }(y，z)}. }[/math]

</math>

数学

For jointly continuous random variables this takes the form

对于联合连续的随机变量，这采取的形式

[math]\displaystyle{ \lt math\gt 数学 \operatorname{I}(X;Y|Z) = \int_{\mathcal{Z}} \int_{\mathcal{Y}} \int_{\mathcal{X}} \operatorname{I}(X;Y|Z) = \int_{\mathcal{Z}} \int_{\mathcal{Y}} \int_{\mathcal{X}} 运算符名称{ i }(x; y | z) int { mathcal { z } int { mathcal { y }{ x } {p_Z(z)\, p_{X,Y|Z}(x,y|z) {p_Z(z)\, p_{X,Y|Z}(x,y|z) { p z (z) ，p { x，y | z }(x，y | z) \log\left[\frac{p_{X,Y|Z}(x,y|z)}{p_{X|Z}\,(x|z)p_{Y|Z}(y|z)}\right]} dx dy dz, \log\left[\frac{p_{X,Y|Z}(x,y|z)}{p_{X|Z}\,(x|z)p_{Y|Z}(y|z)}\right]} dx dy dz, 向左[ frac { x，y | z }(x，y | z)}{ p { x | z } ，(x | z) p { y | z }(y | z)}右]} dx dy dz, }[/math]

</math>

数学

which can be simplified as

可以简化为

[math]\displaystyle{ \lt math\gt 数学 \operatorname{I}(X;Y|Z) = \int_{\mathcal{Z}} \int_{\mathcal{Y}} \int_{\mathcal{X}} \operatorname{I}(X;Y|Z) = \int_{\mathcal{Z}} \int_{\mathcal{Y}} \int_{\mathcal{X}} 运算符名称{ i }(x; y | z) int { mathcal { z } int { mathcal { y }{ x } p_{X,Y,Z}(x,y,z) \log \frac{p_{X,Y,Z}(x,y,z)p_{Z}(z)}{p_{X,Z}(x,z)p_{Y,Z}(y,z)} dx dy dz. p_{X,Y,Z}(x,y,z) \log \frac{p_{X,Y,Z}(x,y,z)p_{Z}(z)}{p_{X,Z}(x,z)p_{Y,Z}(y,z)} dx dy dz. P { x，y，z }(x，y，z) log frac { x，y，z }(x，y，z) p { z }(z)}(x，z) p { y，z }(y，z)} dx dy dz. }[/math]

</math>

数学

Conditioning on a third random variable may either increase or decrease the mutual information, but it is always true that

对第三个随机变量的条件作用可能增加或减少相互信息，但它始终是真实的

[math]\displaystyle{ \operatorname{I}(X;Y|Z) \ge 0 }[/math]

{ i }(x; y | z) ge 0 / math

for discrete, jointly distributed random variables [math]\displaystyle{ X,Y,Z }[/math]. This result has been used as a basic building block for proving other inequalities in information theory.

为离散，联合分布的随机变量数学 x，y，z / math。这个结果已被用作证明信息论中其他不等式的基本构件。

Multivariate mutual information

多元互信息

Several generalizations of mutual information to more than two random variables have been proposed, such as total correlation (or multi-information) and interaction information. The expression and study of multivariate higher-degree mutual-information was achieved in two seemingly independent works: McGill (1954) ^[8] who called these functions “interaction information”, and Hu Kuo Ting (1962) ^[9] who also first proved the possible negativity of mutual-information for degrees higher than 2 and justified algebraically the intuitive correspondence to Venn diagrams ^[10]

Several generalizations of mutual information to more than two random variables have been proposed, such as total correlation (or multi-information) and interaction information. The expression and study of multivariate higher-degree mutual-information was achieved in two seemingly independent works: McGill (1954) who called these functions “interaction information”, and Hu Kuo Ting (1962) who also first proved the possible negativity of mutual-information for degrees higher than 2 and justified algebraically the intuitive correspondence to Venn diagrams

提出了几种将互信息推广到两个以上随机变量的方法，如总相关(或多信息)和交互信息。多元高次互信息的表达和研究是在两部看似独立的著作中完成的: 麦吉尔(1954)将这些函数称为“交互信息” ，胡阔庭(1962)首次证明了高于2次互信息的可能负性，并用代数方法证明了与维恩图的直观对应关系

[math]\displaystyle{ \lt math\gt 数学 \operatorname{I}(X_1;X_1) = \Eta(X_1) \operatorname{I}(X_1;X_1) = \Eta(X_1) { i }(x1; x1) Eta (x1) }[/math]

</math>

数学

and for [math]\displaystyle{ n \gt 1, }[/math]

还有数学1 / 数学

[math]\displaystyle{ \lt math\gt 数学 \operatorname{I}(X_1;\,...\,;X_n) \operatorname{I}(X_1;\,...\,;X_n) 操作者名{ i }(x1; ，... ，; xn) = \operatorname{I}(X_1;\,...\,;X_{n-1}) = \operatorname{I}(X_1;\,...\,;X_{n-1}) 操作者名{ i }(x1; ，... ，; x{ n-1}) - \operatorname{I}(X_1;\,...\,;X_{n-1}|X_n), - \operatorname{I}(X_1;\,...\,;X_{n-1}|X_n), - 操作者名{ i }(x1; ，... ，; x{ n-1} | xn) , }[/math]

</math>

数学

where (as above) we define

(如上所述)我们在哪里定义

[math]\displaystyle{ \lt math\gt 数学 I(X_1;\ldots;X_{n-1}|X_{n}) = \mathbb{E}_{X_{n}} [D_{\mathrm{KL}}( P_{(X_1,\ldots,X_{n-1})|X_{n}} \| P_{X_1|X_{n}} \otimes\cdots\otimes P_{X_{n-1}|X_{n}} )]. I(X_1;\ldots;X_{n-1}|X_{n}) = \mathbb{E}_{X_{n}} [D_{\mathrm{KL}}( P_{(X_1,\ldots,X_{n-1})|X_{n}} \| P_{X_1|X_{n}} \otimes\cdots\otimes P_{X_{n-1}|X_{n}} )]. I (x1; ldots; x { n-1} | x { n }) mathbb { x { n }[ d { mathrum { KL }(p {(x1，ldots，x { n-1}) | x { n } | p { x1 | x { n } n } n 次数{ x { n-1} | x { n }]]。 }[/math]

</math>

数学

(This definition of multivariate mutual information is identical to that of interaction information except for a change in sign when the number of random variables is odd.)

(这种多变量互信息的定义与交互信息的定义相同，只是在随机变量数目为奇数时符号发生了变化。)

Multivariate statistical independence

多元统计独立性

The multivariate mutual-information functions generalize the pairwise independence case that states that [math]\displaystyle{ X_1,X_2 }[/math] if and only if [math]\displaystyle{ I(X_1;X_2)=0 }[/math], to arbitrary numerous variable. n variables are mutually independent if and only if the [math]\displaystyle{ 2^n-n-1 }[/math] mutual information functions vanish [math]\displaystyle{ I(X_1;...;X_k)=0 }[/math] with [math]\displaystyle{ n \ge k \ge 2 }[/math] (theorem 2 ^[10]). In this sense, the [math]\displaystyle{ I(X_1;...;X_k)=0 }[/math] can be used as a refined statistical independence criterion.

The multivariate mutual-information functions generalize the pairwise independence case that states that [math]\displaystyle{ X_1,X_2 }[/math] if and only if [math]\displaystyle{ I(X_1;X_2)=0 }[/math], to arbitrary numerous variable. n variables are mutually independent if and only if the [math]\displaystyle{ 2^n-n-1 }[/math] mutual information functions vanish [math]\displaystyle{ I(X_1;...;X_k)=0 }[/math] with [math]\displaystyle{ n \ge k \ge 2 }[/math] (theorem 2 ). In this sense, the [math]\displaystyle{ I(X_1;...;X_k)=0 }[/math] can be used as a refined statistical independence criterion.

多元互信息函数推广了数学 x1，x2 / math 当且仅当 math i (x1; x2)0 / math 为任意多个变量的成对独立情形。N 个变量是相互独立的当且仅当数学2 ^ n-n-1 / 数学互信息函数消失数学 i (x1; ... ; xk)0 / 数学 n ge k ge 2 / 数学(定理2)。在这个意义上，数学 i (x1; ... ; xk)0 / math 可以用作改进的统计独立性标准。

Applications

申请

For 3 variables, Brenner et al. applied multivariate mutual information to neural coding and called its negativity "synergy" ^[11] and Watkinson et al. applied it to genetic expression ^[12]. For arbitrary k variables, Tapia et al. applied multivariate mutual information to gene expression ^[13] ^[10]). It can be zero, positive, or negative ^[14]. The positivity corresponds to relations generalizing the pairwise correlations, nullity corresponds to a refined notion of independence, and negativity detects high dimensional "emergent" relations and clusterized datapoints ^[13]).

For 3 variables, Brenner et al. applied multivariate mutual information to neural coding and called its negativity "synergy" and Watkinson et al. applied it to genetic expression . For arbitrary k variables, Tapia et al. applied multivariate mutual information to gene expression . The positivity corresponds to relations generalizing the pairwise correlations, nullity corresponds to a refined notion of independence, and negativity detects high dimensional "emergent" relations and clusterized datapoints ).

对于3个变量，Brenner 等人。将多变量互信息应用到神经编码中，并将其负性称为“协同作用”和 Watkinson 等人。应用到基因表达上。对于任意的 k 变量，Tapia 等人。将多元互信息应用于基因表达。正性对应于一般化成对相关性的关系，无效性对应于一个精确的独立性概念，负性检测高维“涌现”关系和聚合数据点)。

One high-dimensional generalization scheme which maximizes the mutual information between the joint distribution and other target variables is found to be useful in feature selection.引用错误：没有找到与</ref>对应的<ref>标签

 | isbn = 978-0-521-86571-5 }}</ref>

/ ref

Mutual information is also used in the area of signal processing as a measure of similarity between two signals. For example, FMI metric^[15] is an image fusion performance measure that makes use of mutual information in order to measure the amount of information that the fused image contains about the source images. The Matlab code for this metric can be found at.^[16]

Mutual information is also used in the area of signal processing as a measure of similarity between two signals. For example, FMI metric is an image fusion performance measure that makes use of mutual information in order to measure the amount of information that the fused image contains about the source images. The Matlab code for this metric can be found at.

互信息也用于信号处理领域，作为两个信号之间相似性的度量。例如，FMI 度量是一种利用互信息来度量融合图像包含的关于源图像的信息量的图像融合性能度量。这个度量的 Matlab 代码可以在。

Directed information

定向信息

Directed information, [math]\displaystyle{ \operatorname{I}\left(X^n \to Y^n\right) }[/math], measures the amount of information that flows from the process [math]\displaystyle{ X^n }[/math] to [math]\displaystyle{ Y^n }[/math], where [math]\displaystyle{ X^n }[/math] denotes the vector [math]\displaystyle{ X_1, X_2, ..., X_n }[/math] and [math]\displaystyle{ Y^n }[/math] denotes [math]\displaystyle{ Y_1, Y_2, ..., Y_n }[/math]. The term directed information was coined by James Massey and is defined as

Directed information, [math]\displaystyle{ \operatorname{I}\left(X^n \to Y^n\right) }[/math], measures the amount of information that flows from the process [math]\displaystyle{ X^n }[/math] to [math]\displaystyle{ Y^n }[/math], where [math]\displaystyle{ X^n }[/math] denotes the vector [math]\displaystyle{ X_1, X_2, ..., X_n }[/math] and [math]\displaystyle{ Y^n }[/math] denotes [math]\displaystyle{ Y_1, Y_2, ..., Y_n }[/math]. The term directed information was coined by James Massey and is defined as

有向信息，数学操作者名称左(x ^ n 到 y ^ n 右) / math，测量从过程数学 x ^ n / math 到数学 y ^ n / math 的信息量，其中数学 x ^ n / math 表示向量数学 x 1，x 2，... ，x n / math 和数学 y ^ n / math 表示数学 y 1，y 2，... ，y / math。定向信息这个术语是由 James Massey 创造的，被定义为

[math]\displaystyle{ \operatorname{I}\left(X^n \to Y^n\right) \lt math\gt \operatorname{I}\left(X^n \to Y^n\right) 数学运算符名称{ i }左(x ^ n 到 y ^ n 右) = \sum_{i=1}^n \operatorname{I}\left(X^i; Y_i|Y^{i-1}\right) }[/math].

= \sum_{i=1}^n \operatorname{I}\left(X^i; Y_i|Y^{i-1}\right)</math>.

(x ^ i; y | y ^ { i-1}右) / math。

Note that if [math]\displaystyle{ n=1 }[/math], the directed information becomes the mutual information. Directed information has many applications in problems where causality plays an important role, such as capacity of channel with feedback.^[17]^[18]

Note that if [math]\displaystyle{ n=1 }[/math], the directed information becomes the mutual information. Directed information has many applications in problems where causality plays an important role, such as capacity of channel with feedback.

注意，如果 math n 1 / math，有向信息成为互信息。有向信息在因果关系问题中有着广泛的应用，如反馈信道容量问题。

Normalized variants

标准化变体

Normalized variants of the mutual information are provided by the coefficients of constraint,模板:Sfn uncertainty coefficient^[19] or proficiency:引用错误：没有找到与</ref>对应的<ref>标签

}}</ref>

{} / ref

[math]\displaystyle{ \lt math\gt 数学 C_{XY} = \frac{\operatorname{I}(X;Y)}{\Eta(Y)} C_{XY} = \frac{\operatorname{I}(X;Y)}{\Eta(Y)} C { XY } frac { operatorname { i }(x; y)}{ Eta (y)} ~~~~\mbox{and}~~~~ ~~~~\mbox{and}~~~~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ C_{YX} = \frac{\operatorname{I}(X;Y)}{\Eta(X)}. C_{YX} = \frac{\operatorname{I}(X;Y)}{\Eta(X)}. C { YX } frac { operatorname { i }(x; y)}{ Eta (x)}. }[/math]

</math>

数学

The two coefficients have a value ranging in [0, 1], but are not necessarily equal. In some cases a symmetric measure may be desired, such as the following redundancy^{[citation needed]} measure:

The two coefficients have a value ranging in [0, 1], but are not necessarily equal. In some cases a symmetric measure may be desired, such as the following redundancy measure:

这两个系数的值范围为[0,1] ，但不一定相等。在某些情况下，可能需要一个对称的度量，例如下面的冗余度量:

[math]\displaystyle{ R = \frac{\operatorname{I}(X;Y)}{\Eta(X) + \Eta(Y)} }[/math]

数学 r frac { operatorname { i }(x; y)}{ Eta (x) + Eta (y)} / math

which attains a minimum of zero when the variables are independent and a maximum value of

当变量是独立的时候，它达到最小值为零，最大值为

[math]\displaystyle{ R_\max = \frac{\min\left\{\Eta(X), \Eta(Y)\right\}}{\Eta(X) + \Eta(Y)} }[/math]

数学 rmax | frac | 左 Eta (x) ，Eta (y) | 右 Eta (x) + Eta (y)} / math

when one variable becomes completely redundant with the knowledge of the other. See also Redundancy (information theory).

when one variable becomes completely redundant with the knowledge of the other. See also Redundancy (information theory).

当一个变量与另一个变量的知识完全多余时。参见冗余(信息论)。

Another symmetrical measure is the symmetric uncertainty 模板:Harv, given by

Another symmetrical measure is the symmetric uncertainty , given by

另一个对称度量是对称不确定度，由

[math]\displaystyle{ U(X, Y) = 2R = 2\frac{\operatorname{I}(X;Y)}{\Eta(X) + \Eta(Y)} }[/math]

Math u (x，y)2r2 frac { operatorname { i }(x; y)}{ Eta (x) + Eta (y)} / math

which represents the harmonic mean of the two uncertainty coefficients [math]\displaystyle{ C_{XY}, C_{YX} }[/math].^[19]

which represents the harmonic mean of the two uncertainty coefficients [math]\displaystyle{ C_{XY}, C_{YX} }[/math].

它表示两个不确定系数的调和平均数。

If we consider mutual information as a special case of the total correlation or dual total correlation, the normalized version are respectively,

如果我们把互信息看作是总相关或对偶总相关的特殊情况，归一化版本分别为,

[math]\displaystyle{ \frac{\operatorname{I}(X;Y)}{\min\left[ \Eta(X),\Eta(Y)\right]} }[/math] and [math]\displaystyle{ \frac{\operatorname{I}(X;Y)}{\Eta(X,Y)} \; . }[/math]

[ | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |

This normalized version also known as Information Quality Ratio (IQR) which quantifies the amount of information of a variable based on another variable against total uncertainty:引用错误：没有找到与</ref>对应的<ref>标签

|year= 2017 }}</ref>

2017年开始 / ref

[math]\displaystyle{ IQR(X, Y) = \operatorname{E}[\operatorname{I}(X;Y)] \lt math\gt IQR(X, Y) = \operatorname{E}[\operatorname{I}(X;Y)] 数学 IQR (x，y)操作者名{ e }[操作者名{ i }(x; y)] = \frac{\operatorname{I}(X;Y)}{\Eta(X, Y)} = \frac{\operatorname{I}(X;Y)}{\Eta(X, Y)} Frac { operatorname { i }(x; y)}{ Eta (x，y)} = \frac{\sum_{x \in X} \sum_{y \in Y} p(x, y) \log {p(x)p(y)}}{\sum_{x \in X} \sum_{y \in Y} p(x, y) \log {p(x, y)}} - 1 }[/math]

= \frac{\sum_{x \in X} \sum_{y \in Y} p(x, y) \log {p(x)p(y)}}{\sum_{x \in X} \sum_{y \in Y} p(x, y) \log {p(x, y)}} - 1</math>

(x，y) log { p (x) p (y)}{ sum { y in x，y) log { p (x) p (y)}-1 / math

There's a normalization引用错误：没有找到与</ref>对应的<ref>标签 which derives from first thinking of mutual information as an analogue to covariance (thus Shannon entropy is analogous to variance). Then the normalized mutual information is calculated akin to the Pearson correlation coefficient,

| url=http://www.jmlr.org/papers/volume3/strehl02a/strehl02a.pdf}}</ref> which derives from first thinking of mutual information as an analogue to covariance (thus Shannon entropy is analogous to variance).  Then the normalized mutual information is calculated akin to the Pearson correlation coefficient,

/ / ref，它起源于最初把互信息看作是协方差的类比(因此香农熵类似于方差)。然后计算归一化互信息类似于皮尔逊相关系数,

[math]\displaystyle{ \lt math\gt 数学 \frac{\operatorname{I}(X;Y)}{\sqrt{\Eta(X)\Eta(Y)}}\; . \frac{\operatorname{I}(X;Y)}{\sqrt{\Eta(X)\Eta(Y)}}\; . Frac { operatorname { i }(x; y)}{ sqrt { Eta (x) Eta (y)}} ;. }[/math]

</math>

数学

Weighted variants

加权变量

In the traditional formulation of the mutual information,

在互信息的传统表述中,

[math]\displaystyle{ \operatorname{I}(X;Y) \lt math\gt \operatorname{I}(X;Y) 数学运营商名称{ i }(x; y) = \sum_{y \in Y} \sum_{x \in X} p(x, y) \log \frac{p(x, y)}{p(x)\,p(y)}, }[/math]

= \sum_{y \in Y} \sum_{x \in X} p(x, y) \log \frac{p(x, y)}{p(x)\,p(y)}, </math>

{{ y }{{ y }{ x，y) log { p (x，y)}{ p (x) ，p (y)} ，/ math

each event or object specified by [math]\displaystyle{ (x, y) }[/math] is weighted by the corresponding probability [math]\displaystyle{ p(x, y) }[/math]. This assumes that all objects or events are equivalent apart from their probability of occurrence. However, in some applications it may be the case that certain objects or events are more significant than others, or that certain patterns of association are more semantically important than others.

each event or object specified by [math]\displaystyle{ (x, y) }[/math] is weighted by the corresponding probability [math]\displaystyle{ p(x, y) }[/math]. This assumes that all objects or events are equivalent apart from their probability of occurrence. However, in some applications it may be the case that certain objects or events are more significant than others, or that certain patterns of association are more semantically important than others.

Math (x，y) / math 指定的每个事件或对象都由相应的概率 math p (x，y) / math 加权。这假设除了发生概率之外，所有对象或事件都是等效的。然而，在某些应用程序中，某些对象或事件可能比其他对象或事件更重要，或者某些关联模式在语义上比其他模式更重要。

For example, the deterministic mapping [math]\displaystyle{ \{(1,1),(2,2),(3,3)\} }[/math] may be viewed as stronger than the deterministic mapping [math]\displaystyle{ \{(1,3),(2,1),(3,2)\} }[/math], although these relationships would yield the same mutual information. This is because the mutual information is not sensitive at all to any inherent ordering in the variable values (Cronbach 1954, Coombs, Dawes & Tversky 1970, Lockhead 1970), and is therefore not sensitive at all to the form of the relational mapping between the associated variables. If it is desired that the former relation—showing agreement on all variable values—be judged stronger than the later relation, then it is possible to use the following weighted mutual information 模板:Harv.

For example, the deterministic mapping [math]\displaystyle{ \{(1,1),(2,2),(3,3)\} }[/math] may be viewed as stronger than the deterministic mapping [math]\displaystyle{ \{(1,3),(2,1),(3,2)\} }[/math], although these relationships would yield the same mutual information. This is because the mutual information is not sensitive at all to any inherent ordering in the variable values (, , ), and is therefore not sensitive at all to the form of the relational mapping between the associated variables. If it is desired that the former relation—showing agreement on all variable values—be judged stronger than the later relation, then it is possible to use the following weighted mutual information .

例如，确定性映射数学{(1,1) ，(2,2) ，(3,3)} / math 可能被视为比确定性映射数学{(1,3) ，(2,1) ，(3,2)} / math 更强，尽管这些关系将产生相同的互信息。这是因为互信息对变量值(，,)的任何固有顺序都不敏感，因此对相关变量之间的关系映射形式一点也不敏感。如果希望判断前一个关系(即对所有变量值的一致性)比后一个关系强，则可以使用下列加权互信息。

[math]\displaystyle{ \operatorname{I}(X;Y) \lt math\gt \operatorname{I}(X;Y) 数学运营商名称{ i }(x; y) = \sum_{y \in Y} \sum_{x \in X} w(x,y) p(x,y) \log \frac{p(x,y)}{p(x)\,p(y)}, }[/math]

= \sum_{y \in Y} \sum_{x \in X} w(x,y) p(x,y) \log \frac{p(x,y)}{p(x)\,p(y)}, </math>

(x，y) p (x，y) log frac { p (x，y)}{ p (x) ，p (y)} ，/ math

which places a weight [math]\displaystyle{ w(x,y) }[/math] on the probability of each variable value co-occurrence, [math]\displaystyle{ p(x,y) }[/math]. This allows that certain probabilities may carry more or less significance than others, thereby allowing the quantification of relevant holistic or Prägnanz factors. In the above example, using larger relative weights for [math]\displaystyle{ w(1,1) }[/math], [math]\displaystyle{ w(2,2) }[/math], and [math]\displaystyle{ w(3,3) }[/math] would have the effect of assessing greater informativeness for the relation [math]\displaystyle{ \{(1,1),(2,2),(3,3)\} }[/math] than for the relation [math]\displaystyle{ \{(1,3),(2,1),(3,2)\} }[/math], which may be desirable in some cases of pattern recognition, and the like. This weighted mutual information is a form of weighted KL-Divergence, which is known to take negative values for some inputs,^[20] and there are examples where the weighted mutual information also takes negative values.引用错误：没有找到与</ref>对应的<ref>标签

}}</ref>

{} / ref

Adjusted mutual information

调整后的相互信息

A probability distribution can be viewed as a partition of a set. One may then ask: if a set were partitioned randomly, what would the distribution of probabilities be? What would the expectation value of the mutual information be? The adjusted mutual information or AMI subtracts the expectation value of the MI, so that the AMI is zero when two different distributions are random, and one when two distributions are identical. The AMI is defined in analogy to the adjusted Rand index of two different partitions of a set.

概率分布可以被看作是集合划分。然后有人可能会问: 如果一个集合被随机分割，概率的分布会是什么？相互信息的期望值是什么？调整后的互信息或 AMI 减去 MI 的期望值，因此当两个不同的分布是随机的时候 AMI 为零，当两个分布是相同的时候 AMI 为零。Ami 的定义类似于一个集合的两个不同分区的调整后的 Rand 指数。

Absolute mutual information

绝对的互信息! ——这一部分与科尔莫戈罗夫的复杂性有关——

Using the ideas of Kolmogorov complexity, one can consider the mutual information of two sequences independent of any probability distribution:

利用柯氏复杂性的思想，我们可以考虑两个序列的互信息，这两个序列独立于任何概率分布序列:

[math]\displaystyle{ \lt math\gt 数学 \operatorname{I}_K(X;Y) = K(X) - K(X|Y). \operatorname{I}_K(X;Y) = K(X) - K(X|Y). 运营商名称{ i } k (x; y) k (x)-k (x | y)。 }[/math]

</math>

数学

To establish that this quantity is symmetric up to a logarithmic factor ([math]\displaystyle{ \operatorname{I}_K(X;Y) \approx \operatorname{I}_K(Y;X) }[/math]) one requires the chain rule for Kolmogorov complexity {{ |bracket_left= ( |bracket_right = ) }}. Approximations of this quantity via compression can be used to define a distance measure to perform a hierarchical clustering of sequences without having any domain knowledge of the sequences {{ |bracket_left= ( |bracket_right = ) }}.

To establish that this quantity is symmetric up to a logarithmic factor ([math]\displaystyle{ \operatorname{I}_K(X;Y) \approx \operatorname{I}_K(Y;X) }[/math]) one requires the chain rule for Kolmogorov complexity . Approximations of this quantity via compression can be used to define a distance measure to perform a hierarchical clustering of sequences without having any domain knowledge of the sequences .

为了证明这个量对称于一个对数因子(math operatorname { i } k (x; y) approx operatorname { i } k (y; x) / math) ，我们需要一个柯氏复杂性的链式规则。通过压缩这个量的近似值可以用来定义一个距离度量来执行一个层次化的序列聚类，而不需要任何关于序列的领域知识。

Linear correlation

线性相关

Unlike correlation coefficients, such as the product moment correlation coefficient, mutual information contains information about all dependence—linear and nonlinear—and not just linear dependence as the correlation coefficient measures. However, in the narrow case that the joint distribution for [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math] is a bivariate normal distribution (implying in particular that both marginal distributions are normally distributed), there is an exact relationship between [math]\displaystyle{ \operatorname{I} }[/math] and the correlation coefficient [math]\displaystyle{ \rho }[/math] 模板:Harv.

Unlike correlation coefficients, such as the product moment correlation coefficient, mutual information contains information about all dependence—linear and nonlinear—and not just linear dependence as the correlation coefficient measures. However, in the narrow case that the joint distribution for [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math] is a bivariate normal distribution (implying in particular that both marginal distributions are normally distributed), there is an exact relationship between [math]\displaystyle{ \operatorname{I} }[/math] and the correlation coefficient [math]\displaystyle{ \rho }[/math] .

互信息不同于相关系数，如乘积矩相关系数，互信息包含所有相关信息ーー线性和非线性ーー而不仅仅是相关系数的线性相关。然而，在数学 x / math 和数学 y / math 的联合分布是二元正态分布(特别是边际分布都是正态分布)的狭义情况下，数学运算子{ i } / math 与相关系数 math / rho / math 之间存在精确的关系。

[math]\displaystyle{ \operatorname{I} = -\frac{1}{2} \log\left(1 - \rho^2\right) }[/math]

数学运算器名称{ i }- frac {1} log left (1- rho ^ 2 right) / math

The equation above can be derived as follows for a bivariate Gaussian:

对于双变量高斯分布，上面的公式可以推导如下:

[math]\displaystyle{ \begin{align} \lt math\gt \begin{align} 数学 begin { align } \begin{pmatrix} \begin{pmatrix} 开始{ pmatrix } X_1 \\ X_1 \\ X 1 X_2 X_2 2 \end{pmatrix} &\sim \mathcal{N} \left( \begin{pmatrix} \end{pmatrix} &\sim \mathcal{N} \left( \begin{pmatrix} 文中给出了一种新的数学模型—— -- 左(开始{ pmatrix } ，其中左(开始{ pmatrix } \mu_1 \\ \mu_1 \\ 什么 \mu_2 \mu_2 2 \end{pmatrix}, \Sigma \right),\qquad \end{pmatrix}, \Sigma \right),\qquad 右边，右边，右边 \Sigma = \begin{pmatrix} \Sigma = \begin{pmatrix} Sigma 开始{ pmatrix } \sigma^2_1 & \rho\sigma_1\sigma_2 \\ \sigma^2_1 & \rho\sigma_1\sigma_2 \\ Sigma ^ 21 & rho sigma 1 sigma 2 \rho\sigma_1\sigma_2 & \sigma^2_2 \rho\sigma_1\sigma_2 & \sigma^2_2 西格玛12和西格玛22 \end{pmatrix} \\ \end{pmatrix} \\ 结束{ pmatrix } \Eta(X_i) &= \frac{1}{2}\log\left(2\pi e \sigma_i^2\right) = \frac{1}{2} + \frac{1}{2}\log(2\pi) + \log\left(\sigma_i\right), \quad i\in\{1, 2\} \\ \Eta(X_i) &= \frac{1}{2}\log\left(2\pi e \sigma_i^2\right) = \frac{1}{2} + \frac{1}{2}\log(2\pi) + \log\left(\sigma_i\right), \quad i\in\{1, 2\} \\ Eta (xi) & frac {1}对数左(2 pi e sigma i ^ 2右) frac {1}对数(2 pi) + 对数左(sigma i 右) ，四对数(1,2} \Eta(X_1, X_2) &= \frac{1}{2}\log\left[(2\pi e)^2|\Sigma|\right] = 1 + \log(2\pi) + \log\left(\sigma_1 \sigma_2\right) + \frac{1}{2}\log\left(1 - \rho^2\right) \\ \Eta(X_1, X_2) &= \frac{1}{2}\log\left[(2\pi e)^2|\Sigma|\right] = 1 + \log(2\pi) + \log\left(\sigma_1 \sigma_2\right) + \frac{1}{2}\log\left(1 - \rho^2\right) \\ Eta (x1，x2) & frac {1}2} log 左[(2 pi e) ^ 2 | Sigma | right ]1 + log (2 pi) + log 左(σ1 σ2右) + frac {1} log 左(1 rho ^ 2右) \end{align} }[/math]

\end{align}</math>

End { align } / math

Therefore,

所以,

[math]\displaystyle{ \lt math\gt 数学 \operatorname{I}\left(X_1; X_2\right) \operatorname{I}\left(X_1; X_2\right) 操作者名{ i }左(x1; x2右) = \Eta\left(X_1\right) + \Eta\left(X_2\right) - \Eta\left(X_1, X_2\right) = \Eta\left(X_1\right) + \Eta\left(X_2\right) - \Eta\left(X_1, X_2\right) Eta 左(x1右) + Eta 左(x2右)- Eta 左(x1，x2右) = -\frac{1}{2}\log\left(1 - \rho^2\right) = -\frac{1}{2}\log\left(1 - \rho^2\right) - frac {1} log left (1- rho ^ 2 right) }[/math]

</math>

数学

For discrete data

对于离散数据

When [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math] are limited to be in a discrete number of states, observation data is summarized in a contingency table, with row variable [math]\displaystyle{ X }[/math] (or [math]\displaystyle{ i }[/math]) and column variable [math]\displaystyle{ Y }[/math] (or [math]\displaystyle{ j }[/math]). Mutual information is one of the measures of association or correlation between the row and column variables. Other measures of association include Pearson's chi-squared test statistics, G-test statistics, etc. In fact, mutual information is equal to G-test statistics divided by [math]\displaystyle{ 2N }[/math], where [math]\displaystyle{ N }[/math] is the sample size.

当数学 x / 数学和数学 y / 数学被限制在一个离散的数字状态时，观察数据被总结成一个列联表，有行变量 math x / math (或 math i / math)和列变量 math y / math (或 math j / math)。互信息是行变量和列变量之间关联或相关性的度量之一。其他衡量相关性的指标包括皮尔森卡方检验统计数据、 g 测试统计数据等。事实上，互信息等于 g 检验统计除以 math 2N / math，其中 math n / math 是样本量。

Applications

申请

In many applications, one wants to maximize mutual information (thus increasing dependencies), which is often equivalent to minimizing conditional entropy. Examples include:

在许多应用程序中，需要最大化相互信息(从而增加依赖关系) ，这通常相当于最小化条件熵。例子包括:

In search engine technology, mutual information between phrases and contexts is used as a feature for k-means clustering to discover semantic clusters (concepts).^[21] For example, the mutual information of a bigram might be calculated as:

{{Equation box 1

{方程式方框1

|indent=::

我不知道你在说什么:

|equation=

方程式

[math]\displaystyle{ \lt math\gt 数学 MI(x,y) = \log \frac{P_{X,Y}(x,y)}{P_X(x) P_Y(y)} \approx log \frac{\frac{f_{XY}}{B}}{\frac{f_X}{U} \frac{f_Y}{U}} MI(x,y) = \log \frac{P_{X,Y}(x,y)}{P_X(x) P_Y(y)} \approx log \frac{\frac{f_{XY}}{B}}{\frac{f_X}{U} \frac{f_Y}{U}} Mi (x，y) log frac { p { x，y }(x，y)}{ p x (x) p y (y)} approx log frac { f { f XY }{ b }{ f f { f }{ y } }[/math]

</math>

数学

|cellpadding= 6

6号手术室

|border

边界

|border colour = #0073CF

0073CF

|background colour=#F5FFFA}}

5 / fffa }

where [math]\displaystyle{ f_{XY} }[/math] is the number of times the bigram xy appears in the corpus, [math]\displaystyle{ f_{X} }[/math] is the number of times the unigram x appears in the corpus, B is the total number of bigrams, and U is the total number of unigrams.^[21]

where [math]\displaystyle{ f_{XY} }[/math] is the number of times the bigram xy appears in the corpus, [math]\displaystyle{ f_{X} }[/math] is the number of times the unigram x appears in the corpus, B is the total number of bigrams, and U is the total number of unigrams.

其中 math f { XY } / math 是 bigram XY 在语料库中出现的次数，math f { x } / math 是 unigram x 在语料库中出现的次数，b 是 bigrams 的总数，u 是 unigrams 的总数。

In telecommunications, the channel capacity is equal to the mutual information, maximized over all input distributions.

Discriminative training procedures for hidden Markov models have been proposed based on the maximum mutual information (MMI) criterion.

RNA secondary structure prediction from a multiple sequence alignment.

Phylogenetic profiling prediction from pairwise present and disappearance of functionally link genes.

Mutual information has been used as a criterion for feature selection and feature transformations in machine learning. It can be used to characterize both the relevance and redundancy of variables, such as the minimum redundancy feature selection.

Mutual information is used in determining the similarity of two different clusterings of a dataset. As such, it provides some advantages over the traditional Rand index.

Mutual information of words is often used as a significance function for the computation of collocations in corpus linguistics. This has the added complexity that no word-instance is an instance to two different words; rather, one counts instances where 2 words occur adjacent or in close proximity; this slightly complicates the calculation, since the expected probability of one word occurring within [math]\displaystyle{ N }[/math] words of another, goes up with [math]\displaystyle{ N }[/math].

Mutual information is used in medical imaging for image registration. Given a reference image (for example, a brain scan), and a second image which needs to be put into the same coordinate system as the reference image, this image is deformed until the mutual information between it and the reference image is maximized.

Detection of phase synchronization in time series analysis

In the infomax method for neural-net and other machine learning, including the infomax-based Independent component analysis algorithm

Average mutual information in delay embedding theorem is used for determining the embedding delay parameter.

Mutual information between genes in expression microarray data is used by the ARACNE algorithm for reconstruction of gene networks.

In statistical mechanics, Loschmidt's paradox may be expressed in terms of mutual information.^[22]^[23] Loschmidt noted that it must be impossible to determine a physical law which lacks time reversal symmetry (e.g. the second law of thermodynamics) only from physical laws which have this symmetry. He pointed out that the H-theorem of Boltzmann made the assumption that the velocities of particles in a gas were permanently uncorrelated, which removed the time symmetry inherent in the H-theorem. It can be shown that if a system is described by a probability density in phase space, then Liouville's theorem implies that the joint information (negative of the joint entropy) of the distribution remains constant in time. The joint information is equal to the mutual information plus the sum of all the marginal information (negative of the marginal entropies) for each particle coordinate. Boltzmann's assumption amounts to ignoring the mutual information in the calculation of entropy, which yields the thermodynamic entropy (divided by Boltzmann's constant).

The mutual information is used to learn the structure of Bayesian networks/dynamic Bayesian networks, which is thought to explain the causal relationship between random variables, as exemplified by the GlobalMIT toolkit:^[24] learning the globally optimal dynamic Bayesian network with the Mutual Information Test criterion.

Popular cost function in decision tree learning.

The mutual information is used in cosmology to test the influence of large-scale environments on galaxy properties in the Galaxy Zoo.

The mutual information was used in Solar Physics to derive the solar differential rotation profile, a travel-time deviation map for sunspots, and a time–distance diagram from quiet-Sun measurements^[25]

Used in Invariant Information Clustering to automatically train neural network classifiers and image segmenters given no labelled data.^[26]

Notes

注释

↑ ^1.0 ^1.1 ^1.2 Cover, T.M.; Thomas, J.A. (1991). Elements of Information Theory (Wiley ed.). ISBN 978-0-471-24195-9. https://archive.org/details/elementsofinform0000cove.
↑ Wolpert, D.H.; Wolf, D.R. (1995). "Estimating functions of probability distributions from a finite set of samples". Physical Review E. 52 (6): 6841–6854. Bibcode:1995PhRvE..52.6841W. CiteSeerX 10.1.1.55.7122. doi:10.1103/PhysRevE.52.6841. PMID 9964199.
↑ Hutter, M. (2001). "Distribution of Mutual Information". Advances in Neural Information Processing Systems 2001.
↑ Archer, E.; Park, I.M.; Pillow, J. (2013). "Bayesian and Quasi-Bayesian Estimators for Mutual Information from Discrete Data". Entropy. 15 (12): 1738–1755. Bibcode:2013Entrp..15.1738A. CiteSeerX 10.1.1.294.4690. doi:10.3390/e15051738.
↑ Wolpert, D.H; DeDeo, S. (2013). "Estimating Functions of Distributions Defined over Spaces of Unknown Size". Entropy. 15 (12): 4668–4699. arXiv:1311.4548. Bibcode:2013Entrp..15.4668W. doi:10.3390/e15114668.
↑ Tomasz Jetka; Karol Nienaltowski; Tomasz Winarski; Slawomir Blonski; Michal Komorowski (2019), "Information-theoretic analysis of multivariate single-cell signaling responses", PLOS Computational Biology, 15 (7): e1007132, arXiv:1808.05581, Bibcode:2019PLSCB..15E7132J, doi:10.1371/journal.pcbi.1007132, PMC 6655862, PMID 31299056
↑ Kraskov, Alexander; Stögbauer, Harald; Andrzejak, Ralph G.; Grassberger, Peter (2003). "Hierarchical Clustering Based on Mutual Information". arXiv:q-bio/0311039. Bibcode:2003q.bio....11039K. {{cite journal}}: Cite journal requires |journal= (help)
↑ McGill, W. (1954). "Multivariate information transmission". Psychometrika. 19 (1): 97–116. doi:10.1007/BF02289159.
↑ Hu, K.T. (1962). "On the Amount of Information". Theory Probab. Appl. 7 (4): 439–447. doi:10.1137/1107041.
↑ ^10.0 ^10.1 ^10.2 Baudot, P.; Tapia, M.; Bennequin, D.; Goaillard, J.M. (2019). "Topological Information Data Analysis". Entropy. 21 (9). 869. arXiv:1907.04242. Bibcode:2019Entrp..21..869B. doi:10.3390/e21090869.
↑ Brenner, N.; Strong, S.; Koberle, R.; Bialek, W. (2000). "Synergy in a Neural Code". Neural Comput. 12 (7): 1531–1552. doi:10.1162/089976600300015259. PMID 10935917.
↑ Watkinson, J.; Liang, K.; Wang, X.; Zheng, T.; Anastassiou, D. (2009). "Inference of Regulatory Gene Interactions from Expression Data Using Three-Way Mutual Information". Chall. Syst. Biol. Ann. N. Y. Acad. Sci. 1158 (1): 302–313. Bibcode:2009NYASA1158..302W. doi:10.1111/j.1749-6632.2008.03757.x. PMID 19348651.
↑ ^13.0 ^13.1 Tapia, M.; Baudot, P.; Formizano-Treziny, C.; Dufour, M.; Goaillard, J.M. (2018). "Neurotransmitter identity and electrophysiological phenotype are genetically coupled in midbrain dopaminergic neurons". Sci. Rep. 8 (1): 13637. Bibcode:2018NatSR...813637T. doi:10.1038/s41598-018-31765-z. PMC 6134142. PMID 30206240.
↑ Hu, K.T. (1962). "On the Amount of Information". Theory Probab. Appl. 7 (4): 439–447. doi:10.1137/1107041.
↑ Haghighat, M. B. A.; Aghagolzadeh, A.; Seyedarabi, H. (2011). "A non-reference image fusion metric based on mutual information of image features". Computers & Electrical Engineering. 37 (5): 744–756. doi:10.1016/j.compeleceng.2011.07.012.
↑ "Feature Mutual Information (FMI) metric for non-reference image fusion - File Exchange - MATLAB Central". www.mathworks.com. Retrieved 4 April 2018.
↑ Massey, James (1990). "Causality, Feedback And Directed Informatio". Proc. 1990 Intl. Symp. on Info. Th. and its Applications, Waikiki, Hawaii, Nov. 27-30, 1990. CiteSeerX 10.1.1.36.5688.
↑ Permuter, Haim Henry; Weissman, Tsachy; Goldsmith, Andrea J. (February 2009). "Finite State Channels With Time-Invariant Deterministic Feedback". IEEE Transactions on Information Theory. 55 (2): 644–662. arXiv:cs/0608070. doi:10.1109/TIT.2008.2009849.
↑ ^19.0 ^19.1 Press, WH; Teukolsky, SA; Vetterling, WT; Flannery, BP (2007). "Section 14.7.3. Conditional Entropy and Mutual Information". Numerical Recipes: The Art of Scientific Computing (3rd ed.). New York: Cambridge University Press. ISBN 978-0-521-88068-8. http://apps.nrbook.com/empanel/index.html#pg=758.
↑ Kvålseth, T. O. (1991). "The relative useful information measure: some comments". Information Sciences. 56 (1): 35–38. doi:10.1016/0020-0255(91)90022-m.
↑ ^21.0 ^21.1 Parsing a Natural Language Using Mutual Information Statistics by David M. Magerman and Mitchell P. Marcus
↑ Hugh Everett Theory of the Universal Wavefunction, Thesis, Princeton University, (1956, 1973), pp 1–140 (page 30)
↑ Everett, Hugh (1957). "Relative State Formulation of Quantum Mechanics". Reviews of Modern Physics. 29 (3): 454–462. Bibcode:1957RvMP...29..454E. doi:10.1103/revmodphys.29.454. Archived from the original on 2011-10-27. Retrieved 2012-07-16.
↑ 模板:Google Code
↑ Keys, Dustin; Kholikov, Shukur; Pevtsov, Alexei A. (February 2015). "Application of Mutual Information Methods in Time Distance Helioseismology". Solar Physics. 290 (3): 659–671. arXiv:1501.05597. Bibcode:2015SoPh..290..659K. doi:10.1007/s11207-015-0650-y.
↑ Invariant Information Clustering for Unsupervised Image Classification and Segmentation by Xu Ji, Joao Henriques and Andrea Vedaldi

References

参考资料

Baudot, P.; Tapia, M.; Bennequin, D.; Goaillard, J.M. (2019). "Topological Information Data Analysis". Entropy. 21 (9). 869. arXiv:1907.04242. Bibcode:2019Entrp..21..869B. doi:10.3390/e21090869.

Cilibrasi

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Http://www.cwi.nl/~paulv/papers/cluster.pdf "Clustering by compression 压缩聚类"] (PDF). IEEE Transactions on Information Theory 美国电气和电子工程师协会信息理论杂志. 51

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}}

}}

Cronbach, L. J. (1954). "On the non-rational application of information measures in psychology". In Quastler, Henry. Information Theory in Psychology: Problems and Methods. Glencoe, Illinois: Free Press. pp. 14–30.

Coombs, C. H.; Dawes, R. M.; Tversky, A. (1970). Mathematical Psychology: An Elementary Introduction. Englewood Cliffs, New Jersey: Prentice-Hall.

Church, Kenneth Ward; Hanks, Patrick (1989). "Word association norms, mutual information, and lexicography". Proceedings of the 27th Annual Meeting of the Association for Computational Linguistics: 76–83. doi:10.3115/981623.981633.

Gel'fand, I.M.; Yaglom, A.M. (1957). "Calculation of amount of information about a random function contained in another such function". American Mathematical Society Translations: Series 2. 12: 199–246. {{cite journal}}: Invalid |ref=harv (help) English translation of original in Uspekhi Matematicheskikh Nauk 12 (1): 3-52.

Guiasu, Silviu (1977). Information Theory with Applications. McGraw-Hill, New York. ISBN 978-0-07-025109-0.

Li

最后一个李, Ming

1 Ming; Vitányi, Paul (February 1997

1997年2月). An introduction to Kolmogorov complexity and its applications

标题: 柯氏复杂性及其应用介绍. New York: Springer-Verlag. ISBN [[Special:BookSources/978-0-387-94868-3

[国际标准图书编号978-0-387-94868-3]|978-0-387-94868-3

[国际标准图书编号978-0-387-94868-3]]].

 | ref=harv }}

| ref harv }

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David J. C. MacKay. Information Theory, Inference, and Learning Algorithms Cambridge: Cambridge University Press, 2003.

(available free online)

Haghighat, M. B. A.; Aghagolzadeh, A.; Seyedarabi, H. (2011). "A non-reference image fusion metric based on mutual information of image features". Computers & Electrical Engineering. 37 (5): 744–756. doi:10.1016/j.compeleceng.2011.07.012.

Athanasios Papoulis. Probability, Random Variables, and Stochastic Processes, second edition. New York: McGraw-Hill, 1984. (See Chapter 15.)

Witten, Ian H. & Frank, Eibe (2005). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Amsterdam. ISBN 978-0-12-374856-0. http://www.cs.waikato.ac.nz/~ml/weka/book.html.

Peng, H.C.; Long, F.; Ding, C. (2005). "Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy". IEEE Transactions on Pattern Analysis and Machine Intelligence. 27 (8): 1226–1238. CiteSeerX 10.1.1.63.5765. doi:10.1109/tpami.2005.159. PMID 16119262. {{cite journal}}: Unknown parameter |lastauthoramp= ignored (help)

Andre S. Ribeiro; Stuart A. Kauffman; Jason Lloyd-Price; Bjorn Samuelsson; Joshua Socolar (2008). "Mutual Information in Random Boolean models of regulatory networks". Physical Review E. 77 (1): 011901. arXiv:0707.3642. Bibcode:2008PhRvE..77a1901R. doi:10.1103/physreve.77.011901. PMID 18351870. {{cite journal}}: Unknown parameter |last-author-amp= ignored (help)

Wells

1 Wells, W.M. III

首先是 w.m。三; Viola

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3 Atsumi, H.

第一个3 h。; Nakajima, S.

4 s.; Kikinis

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第35-51页. doi:10.1016/S1361-8415(01)80004-9. PMID [//pubmed.ncbi.nlm.nih.gov/9873920

9873920 9873920 9873920]. Archived from [http://www.ai.mit.edu/people/sw/papers/mia.pdf

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2008-09-06. Retrieved 2010-08-05

2010-08-05. {{cite journal}}: Check |pmid= value (help); Check |url= value (help); Check date values in: |access-date=, |year=, and |archive-date= (help); Invalid |url-status=dead

状态死机 (help); Text "doi 10.1016 / S1361-8415(01)80004-9" ignored (help); Text "first2 p." ignored (help); Text "first5 r." ignored (help); Text "最后4名中岛" ignored (help); Text "档案-网址 https://web.archive.org/web/20080906201633/http://www.ai.mit.edu/people/sw/papers/mia.pdf" ignored (help); line feed character in |access-date= at position 11 (help); line feed character in |archive-date= at position 11 (help); line feed character in |first1= at position 9 (help); line feed character in |first3= at position 3 (help); line feed character in |first4= at position 3 (help); line feed character in |issue= at position 2 (help); line feed character in |journal= at position 23 (help); line feed character in |last1= at position 6 (help); line feed character in |last2= at position 6 (help); line feed character in |last3= at position 7 (help); line feed character in |last5= at position 8 (help); line feed character in |pages= at position 6 (help); line feed character in |pmid= at position 8 (help); line feed character in |ref= at position 5 (help); line feed character in |title= at position 70 (help); line feed character in |url-status= at position 5 (help); line feed character in |url= at position 47 (help); line feed character in |volume= at position 2 (help); line feed character in |year= at position 5 (help)

}}

}}

Pandey, Biswajit; Sarkar, Suman (2017). "How much a galaxy knows about its large-scale environment?: An information theoretic perspective". Monthly Notices of the Royal Astronomical Society Letters. 467 (1): L6. arXiv:1611.00283. Bibcode:2017MNRAS.467L...6P. doi:10.1093/mnrasl/slw250.

Category:Information theory

范畴: 信息论

Category:Entropy and information

类别: 熵和信息

This page was moved from wikipedia:en:Mutual information. Its edit history can be viewed at 互信息/edithistory

[cover1991-1] 1.0 ^1.1 ^1.2 Cover, T.M.; Thomas, J.A. (1991). Elements of Information Theory (Wiley ed.). ISBN 978-0-471-24195-9. https://archive.org/details/elementsofinform0000cove.

[2] Wolpert, D.H.; Wolf, D.R. (1995). "Estimating functions of probability distributions from a finite set of samples". Physical Review E. 52 (6): 6841–6854. Bibcode:1995PhRvE..52.6841W. CiteSeerX 10.1.1.55.7122. doi:10.1103/PhysRevE.52.6841. PMID 9964199.

[3] Hutter, M. (2001). "Distribution of Mutual Information". Advances in Neural Information Processing Systems 2001.

[4] Archer, E.; Park, I.M.; Pillow, J. (2013). "Bayesian and Quasi-Bayesian Estimators for Mutual Information from Discrete Data". Entropy. 15 (12): 1738–1755. Bibcode:2013Entrp..15.1738A. CiteSeerX 10.1.1.294.4690. doi:10.3390/e15051738.

[5] Wolpert, D.H; DeDeo, S. (2013). "Estimating Functions of Distributions Defined over Spaces of Unknown Size". Entropy. 15 (12): 4668–4699. arXiv:1311.4548. Bibcode:2013Entrp..15.4668W. doi:10.3390/e15114668.

[6] Tomasz Jetka; Karol Nienaltowski; Tomasz Winarski; Slawomir Blonski; Michal Komorowski (2019), "Information-theoretic analysis of multivariate single-cell signaling responses", PLOS Computational Biology, 15 (7): e1007132, arXiv:1808.05581, Bibcode:2019PLSCB..15E7132J, doi:10.1371/journal.pcbi.1007132, PMC 6655862, PMID 31299056

[7] Kraskov, Alexander; Stögbauer, Harald; Andrzejak, Ralph G.; Grassberger, Peter (2003). "Hierarchical Clustering Based on Mutual Information". arXiv:q-bio/0311039. Bibcode:2003q.bio....11039K. {{cite journal}}: Cite journal requires |journal= (help)

[8] McGill, W. (1954). "Multivariate information transmission". Psychometrika. 19 (1): 97–116. doi:10.1007/BF02289159.

[9] Hu, K.T. (1962). "On the Amount of Information". Theory Probab. Appl. 7 (4): 439–447. doi:10.1137/1107041.

[e21090869-10] 10.0 ^10.1 ^10.2 Baudot, P.; Tapia, M.; Bennequin, D.; Goaillard, J.M. (2019). "Topological Information Data Analysis". Entropy. 21 (9). 869. arXiv:1907.04242. Bibcode:2019Entrp..21..869B. doi:10.3390/e21090869.

[11] Brenner, N.; Strong, S.; Koberle, R.; Bialek, W. (2000). "Synergy in a Neural Code". Neural Comput. 12 (7): 1531–1552. doi:10.1162/089976600300015259. PMID 10935917.

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[24] 模板:Google Code

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[iic-26] Invariant Information Clustering for Unsupervised Image Classification and Segmentation by Xu Ji, Joao Henriques and Andrea Vedaldi

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“互信息”的版本间的差异

2020年5月7日 (四) 16:21的版本

Definition

Definition

In terms of PMFs for discrete distributions

In terms of PMFs for discrete distributions

In terms of PDFs for continuous distributions

In terms of PDFs for continuous distributions

Motivation

Motivation

Relation to other quantities

Relation to other quantities

Nonnegativity

Nonnegativity

Symmetry

Symmetry

Relation to conditional and joint entropy

Relation to conditional and joint entropy

Relation to Kullback–Leibler divergence

Relation to Kullback–Leibler divergence

Bayesian estimation of mutual information

Bayesian estimation of mutual information

Independence assumptions

Independence assumptions

Variations

Variations

Metric

Metric

Conditional mutual information

Conditional mutual information

Multivariate mutual information

Multivariate mutual information

Multivariate statistical independence

Multivariate statistical independence

Applications

Applications

Directed information

Directed information

Normalized variants

Normalized variants

Weighted variants

Weighted variants

Adjusted mutual information

Adjusted mutual information

Absolute mutual information

Absolute mutual information

Linear correlation

Linear correlation

For discrete data

For discrete data

Applications

Applications

See also

See also

Notes

Notes

References

References

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