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小第1行：
第1行： − 此词条暂由Henry翻译。+ 第9行：
第9行： − + − <font color="#ff8000"> 微分熵Differential entropy</font>(也称为连续熵)是信息论中的一个概念，最初由香农尝试将(香农)熵的概念扩展到连续的概率分布，香农熵是衡量一个随机变量的平均惊人程度的指标。不幸的是，香农没有推导出这个公式，而只是假设它是离散熵的正确连续模拟，但事实上它不是。+ − <math>h(X_1, \ldots, X_n) = \sum_{i=1}^{n} h(X_i|X_1, \ldots, X_{i-1}) \leq \sum_{i=1}^{n} h(X_i)</math>.+ − + − ==Definition==+ − 定义 − − <math>h(X+c) = h(X)</math>+ − [ math > h (x + c) = h (x) </math > + − In particular, for a constant <math>a</math>+ − 特别是对于一个常量+ − <math>h(aX) = h(X)+ \log |a|</math>+ − H (aX) = h (x) + log | a | </math+ − For a vector valued random variable <math>\mathbf{X}</math> and an invertible (square) matrix <math>\mathbf{A}</math>+ − 对于向量值随机变量 < math > mathbf { x } </math > 和可逆矩阵 < math > mathbf { a } </math > + − + − < math > h (mathbf { a } mathbf { x }) = h (mathbf { x }) + log left (| det mathbf { a } | right) </math > + + + − <math>h(\mathbf{Y}) \leq h(\mathbf{X}) + \int f(x) \log \left\vert \frac{\partial m}{\partial x} \right\vert dx</math>+ − [ math > h (mathbf { y }) leq h (mathbf { x }) + int f (x) log left vert frac { partial m }{ partial x } right vert dx </math > + − where <math>\left\vert \frac{\partial m}{\partial x} \right\vert</math> is the Jacobian of the transformation <math>m</math>.+ − 其中“ math” > “ left vert”{ partial m }{ partial x }“ right vert” >/math > 是变换的雅可比矩阵。+ + + − However, differential entropy does not have other desirable properties: − 然而，微分熵并没有其他令人满意的特性: + + + + 第81行：
第85行： − A modification of differential entropy that addresses these drawbacks is the relative information entropy, also known as the Kullback–Leibler divergence, which includes an invariant measure factor (see limiting density of discrete points).+ − 针对这些缺点，微分熵的一个改进是相对熵，也被称为 Kullback-Leibler 分歧，其中包括一个不变测度因子(见离散点的极限密度)。+ + + + + 第93行：
第101行： − With a normal distribution, differential entropy is maximized for a given variance. A Gaussian random variable has the largest entropy amongst all random variables of equal variance, or, alternatively, the maximum entropy distribution under constraints of mean and variance is the Gaussian.+ − 在一个正态分布下，对于给定的方差，微分熵是最大的。在所有方差相等的随机变量中，高斯型随机变量的熵最大，或者说在均值和方差约束下的最大熵分布是高斯型随机变量。+ 第101行：
第109行： + + − Let <math>g(x)</math> be a Gaussian PDF with mean μ and variance <math>\sigma^2</math> and <math>f(x)</math> an arbitrary PDF with the same variance. Since differential entropy is translation invariant we can assume that <math>f(x)</math> has the same mean of <math>\mu</math> as <math>g(x)</math>. − 设g(x) 是一个高斯分布的 PDF，平均值μ 和方差σ2和f(x)一个任意的 PDF，方差相同。由于微分熵是平移不变的，我们可以假设 f(x) 与g(x）具有相同的平均值。 + + − Consider the Kullback–Leibler divergence between the two distributions − 考虑两个分布之间的 Kullback-Leibler 散度 − + − (f | | g) = int _ {-infty } ^ infty f (x) log left (frac { f (x)}{ g (x)} right) dx =-h (f)-int _ {-infty } ^ infty f (x) log (g (x)) dx+ − − Now note that − − 现在注意 − <math>\begin{align}+ − 1.1.1.2.2.2.2.2.2.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.4.3.3.3.3.3.3.3.3.3.3.3.4.3.3.3.3.3.3.3.3.3+ − − \int_{-\infty}^\infty f(x)\log(g(x)) dx &= \int_{-\infty}^\infty f(x)\log\left( \frac{1}{\sqrt{2\pi\sigma^2}}e^{-\frac{(x-\mu)^2}{2\sigma^2}}\right) dx \\ − − Int _ {-infty } ^ infty f (x) log (g (x)) dx & = int _ {-infty } ^ infty f (x) log left (frac {1}{ sqrt {2 pi sigma ^ 2} e ^ {-frac {(x-mu) ^ 2}{2 sigma ^ 2} right) dx − − &= \int_{-\infty}^\infty f(x) \log\frac{1}{\sqrt{2\pi\sigma^2}} dx + \log(e)\int_{-\infty}^\infty f(x)\left( -\frac{(x-\mu)^2}{2\sigma^2}\right) dx \\ − − & = int _ {-infty } ^ infty f (x) log frac {1}{ sqrt {2 pi sigma ^ 2} dx + log (e) int _ {-infty } ^ infty f (x) left (- frac {(x-mu) ^ 2}{2 sigma ^ 2} right) dx − − &= -\tfrac{1}{2}\log(2\pi\sigma^2) - \log(e)\frac{\sigma^2}{2\sigma^2} \\ − − & =-tfrac {1}{2} log (2 pi sigma ^ 2)-log (e) frac { sigma ^ 2}{2 sigma ^ 2} − − &= -\tfrac{1}{2}\left(\log(2\pi\sigma^2) + \log(e)\right) \\ − − & =-tfrac {1}{2}左(log (2 pi sigma ^ 2) + log (e) right) − − &= -\tfrac{1}{2}\log(2\pi e \sigma^2) \\ − − & =-tfrac {1}{2} log (2 pi e sigma ^ 2) − &= -h(g)+ − & =-h (g)+ − \end{align}</math>+ − 结束{ align } </math >+ − because the result does not depend on <math>f(x)</math> other than through the variance. Combining the two results yields+ − 因为结果并不依赖于f(x)，而是通过方差。将这两个结果结合起来就会产生结果+ − <math> h(g) - h(f) \geq 0 \!</math>+ − [数学]-[数学]+ − with equality when <math>f(x)=g(x)</math> following from the properties of Kullback–Leibler divergence.+ − − 当f (x) = g (x)遵循 Kullback-Leibler 分歧的性质时。 − + − ==Properties of differential entropy== − 微分熵的性质 − * For probability densities <math>f</math> and <math>g</math>, the [[Kullback–Leibler divergence]] <math>D_{KL}(f || g)</math> is greater than or equal to 0 with equality only if <math>f=g</math> [[almost everywhere]]. Similarly, for two random variables <math>X</math> and <math>Y</math>, <math>I(X;Y) \ge 0</math> and <math>h(X|Y) \le h(X)</math> with equality [[if and only if]] <math>X</math> and <math>Y</math> are [[Statistical independence|independent]]. − This result may also be demonstrated using the variational calculus. A Lagrangian function with two Lagrangian multipliers may be defined as: − 这个结果也可以用变分法来证明。具有两个拉格朗日乘数的拉格朗日函数可定义为:+ − − * The chain rule for differential entropy holds as in the discrete case<ref name="cover_thomas" />{{rp|253}} − − ::<math>h(X_1, \ldots, X_n) = \sum_{i=1}^{n} h(X_i|X_1, \ldots, X_{i-1}) \leq \sum_{i=1}^{n} h(X_i)</math>. − − <math>L=\int_{-\infty}^\infty g(x)\ln(g(x))\,dx-\lambda_0\left(1-\int_{-\infty}^\infty g(x)\,dx\right)-\lambda\left(\sigma^2-\int_{-\infty}^\infty g(x)(x-\mu)^2\,dx\right)</math> − − < math > l = int _ {-infty } ^ infty g (x) ln (g (x)) ，dx-lambda _ 0 left (1-int _ {-infty } ^ infty g (x) ，dx 右)-lambda left (sigma ^ 2-int _ {-infty } ^ infty g (x)(x-mu) ^ 2，dx 右) </math > − − * Differential entropy is translation invariant, i.e. for a constant <math>c</math>.<ref name="cover_thomas" />{{rp|253}} − − ::<math>h(X+c) = h(X)</math> − − where g(x) is some function with mean μ. When the entropy of g(x) is at a maximum and the constraint equations, which consist of the normalization condition <math>\left(1=\int_{-\infty}^\infty g(x)\,dx\right)</math> and the requirement of fixed variance <math>\left(\sigma^2=\int_{-\infty}^\infty g(x)(x-\mu)^2\,dx\right)</math>, are both satisfied, then a small variation δg(x) about g(x) will produce a variation δL about L which is equal to zero: − − 其中 g (x)是平均 μ 的函数。当 g (x)的熵处于最大值时，由归一化条件 1=∫∞−∞g(x)dx和固定方差σ2=∫∞−∞g(x)(x−μ)2dx组成的约束方程都满足时，那么关于 g (x)的一个小变化 δg (x)将产生一个等于零的关于L的变化δL: − − * Differential entropy is in general not invariant under arbitrary invertible maps. − − :: In particular, for a constant <math>a</math> − − <math>0=\delta L=\int_{-\infty}^\infty \delta g(x)\left (\ln(g(x))+1+\lambda_0+\lambda(x-\mu)^2\right )\,dx</math> − − 0 = delta l = int _ {-infty } ^ infty delta g (x) left (ln (g (x)) + 1 + lambda _ 0 + lambda (x-mu) ^ 2 right) ，dx </math > − − :::<math>h(aX) = h(X)+ \log |a|</math> − − :: For a vector valued random variable <math>\mathbf{X}</math> and an invertible (square) [[matrix (mathematics)|matrix]] <math>\mathbf{A}</math> − − Since this must hold for any small δg(x), the term in brackets must be zero, and solving for g(x) yields: − − 因为这对任何小的 δg (x)都成立，括号中的项必须为零，求 g (x)的结果是: − − :::<math>h(\mathbf{A}\mathbf{X})=h(\mathbf{X})+\log \left( |\det \mathbf{A}| \right)</math><ref name="cover_thomas" />{{rp|253}} − − * In general, for a transformation from a random vector to another random vector with same dimension <math>\mathbf{Y}=m \left(\mathbf{X}\right)</math>, the corresponding entropies are related via − − <math>g(x)=e^{-\lambda_0-1-\lambda(x-\mu)^2}</math> − − < math > g (x) = e ^ {-lambda _ 0-1-lambda (x-mu) ^ 2} </math > − − ::<math>h(\mathbf{Y}) \leq h(\mathbf{X}) + \int f(x) \log \left\vert \frac{\partial m}{\partial x} \right\vert dx</math> − − :where <math>\left\vert \frac{\partial m}{\partial x} \right\vert</math> is the [[Jacobian matrix and determinant|Jacobian]] of the transformation <math>m</math>.<ref>{{cite web |title=proof of upper bound on differential entropy of f(X) |work=[[Stack Exchange]] |date=April 16, 2016 |url=https://math.stackexchange.com/q/1745670 }}</ref> The above inequality becomes an equality if the transform is a bijection. Furthermore, when <math>m</math> is a rigid rotation, translation, or combination thereof, the Jacobian determinant is always 1, and <math>h(Y)=h(X)</math>. − − Using the constraint equations to solve for λ<sub>0</sub> and λ yields the normal distribution: − − 用约束方程求解 λ0和 λ 得到正态分布: − − * If a random vector <math>X \in \mathbb{R}^n</math> has mean zero and [[covariance]] matrix <math>K</math>, <math>h(\mathbf{X}) \leq \frac{1}{2} \log(\det{2 \pi e K}) = \frac{1}{2} \log[(2\pi e)^n \det{K}]</math> with equality if and only if <math>X</math> is [[Multivariate normal distribution#Joint normality|jointly gaussian]] (see [[#Maximization in the normal distribution|below]]).<ref name="cover_thomas" />{{rp|254}} − − − − − − < math > g (x) = frac {1}{ sqrt {2 pi sigma ^ 2} e ^ {-frac {(x-mu) ^ 2}{2 sigma ^ 2}} </math > − − However, differential entropy does not have other desirable properties: − 然而，微分熵并没有期望的性质 − * It is not invariant under [[change of variables]], and is therefore most useful with dimensionless variables. − 它在变量变化下不是不变的，因此对无量纲变量最有用 − * It can be negative. − 它可以为负 − Let <math>X</math> be an exponentially distributed random variable with parameter <math>\lambda</math>, that is, with probability density function − − 设 x 是一个指数分布的随机变量，它的参数是 λ，也就是概率密度函数 − − A modification of differential entropy that addresses these drawbacks is the '''relative information entropy''', also known as the Kullback–Leibler divergence, which includes an [[invariant measure]] factor (see [[limiting density of discrete points]]). − − − − <math>f(x) = \lambda e^{-\lambda x} \mbox{ for } x \geq 0.</math> − − { for } x geq 0. </math > − − ==Maximization in the normal distribution== − 正态分布中的最大化 − ===Theorem=== − 理论 − Its differential entropy is then − 它的微分熵就会 − With a [[normal distribution]], differential entropy is maximized for a given variance. A Gaussian random variable has the largest entropy amongst all random variables of equal variance, or, alternatively, the maximum entropy distribution under constraints of mean and variance is the Gaussian.<ref name="cover_thomas" />{{rp|255}} − 对于正态分布，对于给定的方差，微分熵是最大的。在所有等方差随机变量中，高斯随机变量的熵最大，或者在均值和方差约束下的最大熵分布是高斯分布 − {| − − {| + + 第295行：
第189行： − + − 证明 − | <math>h_e(X)\,</math> − + − − Let <math>g(x)</math> be a [[Normal distribution|Gaussian]] [[Probability density function|PDF]] with mean μ and variance <math>\sigma^2</math> and <math>f(x)</math> an arbitrary [[Probability density function|PDF]] with the same variance. Since differential entropy is translation invariant we can assume that <math>f(x)</math> has the same mean of <math>\mu</math> as <math>g(x)</math>. − − − − + + 第313行：
第201行： − Consider the [[Kullback–Leibler divergence]] between the two distributions+ − − − + − :<math> 0 \leq D_{KL}(f || g) = \int_{-\infty}^\infty f(x) \log \left( \frac{f(x)}{g(x)} \right) dx = -h(f) - \int_{-\infty}^\infty f(x)\log(g(x)) dx.</math>+ − | <math>= -\left(\int_0^\infty (\log \lambda)\lambda e^{-\lambda x}\,dx + \int_0^\infty (-\lambda x) \lambda e^{-\lambda x}\,dx\right) </math>+ − − | < math > =-left (int _ 0 ^ infty (log lambda) lambda e ^ {-lambda x } ，dx + int _ 0 ^ infty (- lambda x) lambda e ^ {-lambda x } ，dx right) </math > − − Now note that 第331行：
第213行： − + − + − + − \int_{-\infty}^\infty f(x)\log(g(x)) dx &= \int_{-\infty}^\infty f(x)\log\left( \frac{1}{\sqrt{2\pi\sigma^2}}e^{-\frac{(x-\mu)^2}{2\sigma^2}}\right) dx \\+ − − | <math>= -\log \lambda \int_0^\infty f(x)\,dx + \lambda E[X]</math> − − | < math > =-log lambda int _ 0 ^ infty f (x) ，dx + lambda e [ x ] </math > − − &= \int_{-\infty}^\infty f(x) \log\frac{1}{\sqrt{2\pi\sigma^2}} dx + \log(e)\int_{-\infty}^\infty f(x)\left( -\frac{(x-\mu)^2}{2\sigma^2}\right) dx \\ 第349行：
第225行： − &= -\tfrac{1}{2}\log(2\pi\sigma^2) - \log(e)\frac{\sigma^2}{2\sigma^2} \\+ − + − − − − &= -\tfrac{1}{2}\left(\log(2\pi\sigma^2) + \log(e)\right) \\ − − − − | < math > =-log lambda + 1，. </math > − − &= -\tfrac{1}{2}\log(2\pi e \sigma^2) \\ − − |} − − |} − − &= -h(g) − − \end{align}</math> − − Here, <math>h_e(X)</math> was used rather than <math>h(X)</math> to make it explicit that the logarithm was taken to base e, to simplify the calculation. − − 在这里，使用he(X)而不是h(X) 来明确对数是以 e 为底，以简化计算。 − − because the result does not depend on <math>f(x)</math> other than through the variance. Combining the two results yields − − :<math> h(g) - h(f) \geq 0 \!</math> − − with equality when <math>f(x)=g(x)</math> following from the properties of Kullback–Leibler divergence. − − The differential entropy yields a lower bound on the expected squared error of an estimator. For any random variable <math>X</math> and estimator <math>\widehat{X}</math> the following holds: − − 对于估计量的预期平方误差，微分熵产生一个下限。对于任何随机变量x和估计量Xˆ 下面的值: − − − − <math>\operatorname{E}[(X - \widehat{X})^2] \ge \frac{1}{2\pi e}e^{2h(X)}</math> − − (x-widehat { x }) ^ 2] ge frac {1}{2 pi e } e ^ {2 h (x)} </math > − − ===Alternative proof=== − 替代证明 − with equality if and only if <math>X</math> is a Gaussian random variable and <math>\widehat{X}</math> is the mean of <math>X</math>. − − 当且仅当x是一个 Gaussian 随机变量，而x 是Xˆ 的平均值。 − − This result may also be demonstrated using the [[variational calculus]]. A Lagrangian function with two [[Lagrangian multiplier]]s may be defined as: − − − − :<math>L=\int_{-\infty}^\infty g(x)\ln(g(x))\,dx-\lambda_0\left(1-\int_{-\infty}^\infty g(x)\,dx\right)-\lambda\left(\sigma^2-\int_{-\infty}^\infty g(x)(x-\mu)^2\,dx\right)</math> − − In the table below <math>\Gamma(x) = \int_0^{\infty} e^{-t} t^{x-1} dt</math> is the gamma function, <math>\psi(x) = \frac{d}{dx} \ln\Gamma(x)=\frac{\Gamma'(x)}{\Gamma(x)}</math> is the digamma function, <math>B(p,q) = \frac{\Gamma(p)\Gamma(q)}{\Gamma(p+q)}</math> is the beta function, and γ<sub>E</sub> is Euler's constant.<math>- (\beta-1)[\psi(\beta) - \psi(\alpha + \beta)] \, </math>||<math>[0,1]\,</math> − − 在下面的表格中，Gamma (x) = int _ 0 ^ { infty } e ^ {-t } t ^ { x-1} dt </math > 是 Gamma 函数,{ math > psi (x) = frac { d }{ dx } ln Gamma (x) = frac { Gamma’(x)}{ Gamma (x)} </math > 是双伽玛函数,b (p，q) = frac { Gamma (p) Gamma (q)}{ Gamma (p + q)} </math > 是 β 函数，γ < sub > e </sub > 是欧拉常数。[ math ]-(beta-1)[ psi (beta)-psi (alpha + beta)] | | < math > [0,1] ，</math > + + 第413行：
第237行： − where ''g(x)'' is some function with mean μ. When the entropy of ''g(x)'' is at a maximum and the constraint equations, which consist of the normalization condition <math>\left(1=\int_{-\infty}^\infty g(x)\,dx\right)</math> and the requirement of fixed variance <math>\left(\sigma^2=\int_{-\infty}^\infty g(x)(x-\mu)^2\,dx\right)</math>, are both satisfied, then a small variation δ''g''(''x'') about ''g(x)'' will produce a variation δ''L'' about ''L'' which is equal to zero:+ 第419行：
第243行： − + 第425行：
第249行： − :<math>0=\delta L=\int_{-\infty}^\infty \delta g(x)\left (\ln(g(x))+1+\lambda_0+\lambda(x-\mu)^2\right )\,dx</math>+ − + − + 第437行：
第261行： − Since this must hold for any small δ''g''(''x''), the term in brackets must be zero, and solving for ''g(x)'' yields:+ 第443行：
第267行： + + − |- − :<math>g(x)=e^{-\lambda_0-1-\lambda(x-\mu)^2}</math> 第455行：
第279行： − + 第461行：
第285行： − Using the constraint equations to solve for λ<sub>0</sub> and λ yields the normal distribution:+ 第467行：
第291行： − + 第473行：
第297行： − :<math>g(x)=\frac{1}{\sqrt{2\pi\sigma^2}}e^{-\frac{(x-\mu)^2}{2\sigma^2}}</math>+ 第485行：
第309行： − + − 例子：指数分布+ − Let <math>X</math> be an [[exponential distribution|exponentially distributed]] random variable with parameter <math>\lambda</math>, that is, with probability density function+ 第497行：
第321行： − + 第503行：
第327行： − :<math>f(x) = \lambda e^{-\lambda x} \mbox{ for } x \geq 0.</math>+ 第509行：
第333行： − + 第515行：
第339行： − Its differential entropy is then+ 第521行：
第345行： − {|+ 第527行：
第351行： − |-+ 第533行：
第357行： − | <math>h_e(X)\,</math>+ − + − | <math>=-\int_0^\infty \lambda e^{-\lambda x} \log (\lambda e^{-\lambda x})\,dx</math>+ 第545行：
第369行： − |-+ 第551行：
第375行： − |+ 第557行：
第381行： − | <math>= -\left(\int_0^\infty (\log \lambda)\lambda e^{-\lambda x}\,dx + \int_0^\infty (-\lambda x) \lambda e^{-\lambda x}\,dx\right) </math>+ − + − |-+ 第569行：
第393行： − |+ 第575行：
第399行： − | <math>= -\log \lambda \int_0^\infty f(x)\,dx + \lambda E[X]</math>+ 第581行：
第405行： − |-+ 第587行：
第411行： − |+ 第593行：
第417行： − | <math>= -\log\lambda + 1\,.</math>+ 第599行：
第423行： − |}+ 第605行：
第429行： + + − |- − Here, <math>h_e(X)</math> was used rather than <math>h(X)</math> to make it explicit that the logarithm was taken to base ''e'', to simplify the calculation. 第617行：
第441行： + + − f_X(\vec{x}) =</math><br /><math> \frac{\exp \left( -\frac{1}{2} ( \vec{x} - \vec{\mu})^\top \Sigma^{-1}\cdot(\vec{x} - \vec{\mu}) \right)} {(2\pi)^{N/2} \left|\Sigma\right|^{1/2}}</math> || <math>\frac{1}{2}\ln\{(2\pi e)^{N} \det(\Sigma)\}</math>||<math>\mathbb{R}^N</math>+ − F _ x (vec { x }) = </math > < br/> < math > frac { exp left (- frac {1}{2}(vec { x }-vec { mu }) ^ top Sigma ^ {-1} cdot (vec { x }-vec { mu }) right)}{(2 pi) ^ { N/2}左 Sigma | right | ^ {1/2} < | < math > | < < | < math > frac {1}{ ln (2 pi e){{ n } | math < | | | | > 数学 < bb > + − ==Relation to estimator error== − 与估计量误差的联系 − The differential entropy yields a lower bound on the expected squared error of an [[estimator]]. For any random variable <math>X</math> and estimator <math>\widehat{X}</math> the following holds:<ref name="cover_thomas" />+ 第635行：
第459行： − + + − with equality if and only if <math>X</math> is a Gaussian random variable and <math>\widehat{X}</math> is the mean of <math>X</math>. 第643行：
第467行： + − + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + − [数学] h =-sum _ i hf (ih) log (f (ih)-sum hf (ih) log (h) + + − In the table below <math>\Gamma(x) = \int_0^{\infty} e^{-t} t^{x-1} dt</math> is the [[gamma function]], <math>\psi(x) = \frac{d}{dx} \ln\Gamma(x)=\frac{\Gamma'(x)}{\Gamma(x)}</math> is the [[digamma function]], <math>B(p,q) = \frac{\Gamma(p)\Gamma(q)}{\Gamma(p+q)}</math> is the [[beta function]], and γ<sub>''E''</sub> is [[Euler-Mascheroni constant|Euler's constant]].<ref>{{cite journal |last1=Park |first1=Sung Y. |last2=Bera |first2=Anil K. |year=2009 |title=Maximum entropy autoregressive conditional heteroskedasticity model |journal=Journal of Econometrics |publisher=Elsevier |url=http://www.wise.xmu.edu.cn/Master/Download/..%5C..%5CUploadFiles%5Cpaper-masterdownload%5C2009519932327055475115776.pdf |accessdate=2011-06-02 |archive-url=https://web.archive.org/web/20160307144515/http://wise.xmu.edu.cn/uploadfiles/paper-masterdownload/2009519932327055475115776.pdf |archive-date=2016-03-07 |url-status=dead }}</ref>{{rp|219–230}} − {| class="wikitable" style="background:white"+ − 右边的第一个术语近似于微分熵，而第二个术语近似于log(h)。请注意，这个过程表明，连续随机变量的离散意义上的熵应该是“无穷”。+ + + − |+ Table of differential entropies − |- − ! Distribution Name !! Probability density function (pdf) !! Entropy in [[Nat (unit)|nat]]s || Support+ − |- − | [[Uniform distribution (continuous)|Uniform]] || <math>f(x) = \frac{1}{b-a}</math> || <math>\ln(b - a) \,</math> ||<math>[a,b]\,</math> − |-+ − | [[Normal distribution|Normal]] || <math>f(x) = \frac{1}{\sqrt{2\pi\sigma^2}} \exp\left(-\frac{(x-\mu)^2}{2\sigma^2}\right)</math> || <math>\ln\left(\sigma\sqrt{2\,\pi\,e}\right) </math>||<math>(-\infty,\infty)\,</math>+ − | [[Exponential distribution|Exponential]] || <math>f(x) = \lambda \exp\left(-\lambda x\right)</math> || <math>1 - \ln \lambda \, </math>||<math>[0,\infty)\,</math>+ + + − + + + − + − − |- 第691行：
第549行： − | [[Cauchy distribution|Cauchy]] || <math>f(x) = \frac{\gamma}{\pi} \frac{1}{\gamma^2 + x^2}</math> || <math>\ln(4\pi\gamma) \, </math>||<math>(-\infty,\infty)\,</math>+
Moved page from wikipedia:en:Differential entropy (history)
此词条暂由彩云小译翻译，翻译字数共1092，未经人工整理和审校，带来阅读不便，请见谅。
{{Short description|Concept in information theory}}
{{Short description|Concept in information theory}}
'''Differential entropy''' (also referred to as '''continuous entropy''') is a concept in [[information theory]] that began as an attempt by Shannon to extend the idea of (Shannon) [[information entropy|entropy]], a measure of average [[surprisal]] of a [[random variable]], to continuous [[probability distribution]]s. Unfortunately, Shannon did not derive this formula, and rather just assumed it was the correct continuous analogue of discrete entropy, but it is not.<ref>{{cite journal |author=Jaynes, E.T. |authorlink=Edwin Thompson Jaynes |title=Information Theory And Statistical Mechanics |journal=Brandeis University Summer Institute Lectures in Theoretical Physics |volume=3 |issue=sect. 4b |year=1963 |url=http://bayes.wustl.edu/etj/articles/brandeis.pdf |format=PDF}}</ref>{{rp|181–218}} The actual continuous version of discrete entropy is the [[limiting density of discrete points]] (LDDP). Differential entropy (described here) is commonly encountered in the literature, but it is a limiting case of the LDDP, and one that loses its fundamental association with discrete [[information entropy|entropy]].
'''Differential entropy''' (also referred to as '''continuous entropy''') is a concept in [[information theory]] that began as an attempt by Shannon to extend the idea of (Shannon) [[information entropy|entropy]], a measure of average [[surprisal]] of a [[random variable]], to continuous [[probability distribution]]s. Unfortunately, Shannon did not derive this formula, and rather just assumed it was the correct continuous analogue of discrete entropy, but it is not.<ref>{{cite journal |author=Jaynes, E.T. |authorlink=Edwin Thompson Jaynes |title=Information Theory And Statistical Mechanics |journal=Brandeis University Summer Institute Lectures in Theoretical Physics |volume=3 |issue=sect. 4b |year=1963 |url=http://bayes.wustl.edu/etj/articles/brandeis.pdf |format=PDF}}</ref>{{rp|181–218}} The actual continuous version of discrete entropy is the [[limiting density of discrete points]] (LDDP). Differential entropy (described here) is commonly encountered in the literature, but it is a limiting case of the LDDP, and one that loses its fundamental association with discrete [[information entropy|entropy]].
Differential entropy (also referred to as continuous entropy) is a concept in information theory that began as an attempt by Shannon to extend the idea of (Shannon) entropy, a measure of average surprisal of a random variable, to continuous probability distributions. Unfortunately, Shannon did not derive this formula, and rather just assumed it was the correct continuous analogue of discrete entropy, but it is not.
Differential entropy (also referred to as continuous entropy) is a concept in information theory that began as an attempt by Shannon to extend the idea of (Shannon) entropy, a measure of average surprisal of a random variable, to continuous probability distributions. Unfortunately, Shannon did not derive this formula, and rather just assumed it was the correct continuous analogue of discrete entropy, but it is not. The actual continuous version of discrete entropy is the limiting density of discrete points (LDDP). Differential entropy (described here) is commonly encountered in the literature, but it is a limiting case of the LDDP, and one that loses its fundamental association with discrete entropy.
微分熵(也称为连续熵)是信息论中的一个概念，最初由香农尝试将(香农)熵的概念扩展到连续的概率分布，香农熵是衡量一个随机变量的平均惊人程度的指标。不幸的是，香农没有推导出这个公式，而只是假设它是离散熵的正确连续模拟，但它不是。离散熵的实际连续形式是离散点的极限密度(LDDP)。微分熵(在这里描述)在文献中经常遇到，但是它是 LDDP 的一个极限情况，并且失去了它与离散熵的基本联系。
==Definition==
< math > h (x _ 1，ldots，xn) = sum _ { i = 1} ^ { n } h (x _ i | x _ 1，ldots，x _ { i-1}) leq sum _ { i = 1} ^ { n } h (x _ i) </math > .
Let <math>X</math> be a random variable with a [[probability density function]] <math>f</math> whose [[support (mathematics)|support]] is a set <math>\mathcal X</math>. The ''differential entropy'' <math>h(X)</math> or <math>h(f)</math> is defined as<ref name="cover_thomas">{{cite book|first1=Thomas M.|first2=Joy A.|last1=Cover|last2=Thomas|isbn=0-471-06259-6|title=Elements of Information Theory|year=1991|publisher=Wiley|location=New York|url=https://archive.org/details/elementsofinform0000cove|url-access=registration}}</ref>{{rp|243}}
Let <math>X</math> be a random variable with a probability density function <math>f</math> whose support is a set <math>\mathcal X</math>. The differential entropy <math>h(X)</math> or <math>h(f)</math> is defined as
Let <math>X</math> be a random variable with a [[probability density function]] <math>f</math> whose [[support (mathematics)|support]] is a set <math>\mathcal X</math>. The ''differential entropy'' <math>h(X)</math> or <math>h(f)</math> is defined as<ref name="cover_thomas">{{cite book|first1=Thomas M.|first2=Joy A.|last1=Cover|last2=Thomas|isbn=0-471-06259-6|title=Elements of Information Theory|year=1991|publisher=Wiley|location=New York|url=https://archive.org/details/elementsofinform0000cove|url-access=registration}}</ref>{{rp|243}}
设 x 是一个随机变量，其概率密度函数是一个集合。微分熵被定义为
{{Equation box 1
{{Equation box 1
{{Equation box 1
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|equation = <math>h(X) = -\int_\mathcal{X} f(x)\log f(x)\,dx</math>
|equation = <math>h(X) = -\int_\mathcal{X} f(x)\log f(x)\,dx</math>
|equation = <math>h(X) = -\int_\mathcal{X} f(x)\log f(x)\,dx</math>
<math>h(\mathbf{A}\mathbf{X})=h(\mathbf{X})+\log \left( |\det \mathbf{A}| \right)</math>
| 等式 = < math > h (x) =-int _ mathcal { x } f (x) log f (x) ，dx </math >
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For probability distributions which don't have an explicit density function expression, but have an explicit [[quantile function]] expression, <math>Q(p)</math>, then <math>h(Q)</math> can be defined in terms of the derivative of <math>Q(p)</math> i.e. the quantile density function <math>Q'(p)</math> as <ref>{{Citation |last1=Vasicek |first1=Oldrich |year=1976 |title=A Test for Normality Based on Sample Entropy |journal=[[Journal of the Royal Statistical Society, Series B]] |volume=38 |issue=1 |jstor=2984828 |postscript=. }}</ref>{{rp|54–59}}
For probability distributions which don't have an explicit density function expression, but have an explicit [[quantile function]] expression, <math>Q(p)</math>, then <math>h(Q)</math> can be defined in terms of the derivative of <math>Q(p)</math> i.e. the quantile density function <math>Q'(p)</math> as <ref>{{Citation |last1=Vasicek |first1=Oldrich |year=1976 |title=A Test for Normality Based on Sample Entropy |journal=[[Journal of the Royal Statistical Society, Series B]] |volume=38 |issue=1 |jstor=2984828 |postscript=. }}</ref>{{rp|54–59}}
For probability distributions which don't have an explicit density function expression, but have an explicit quantile function expression, <math>Q(p)</math>, then <math>h(Q)</math> can be defined in terms of the derivative of <math>Q(p)</math> i.e. the quantile density function <math>Q'(p)</math> as
对于没有明确的密度函数表达式，但是有明确的分位函数表达式的概率分布，那么可以用 < math > q (p) </math > 的导数来定义 < math > q (p) </math > 。分位数密度函数 < math > q’(p) </math >
:<math>h(Q) = \int_0^1 \log Q'(p)\,dp</math>.
:<math>h(Q) = \int_0^1 \log Q'(p)\,dp</math>.
<math>h(Q) = \int_0^1 \log Q'(p)\,dp</math>.
< math > h (q) = int _ 0 ^ 1 log q’(p) ，dp </math > 。
As with its discrete analog, the units of differential entropy depend on the base of the [[logarithm]], which is usually 2 (i.e., the units are [[bit]]s). See [[logarithmic units]] for logarithms taken in different bases. Related concepts such as [[joint entropy|joint]], [[conditional entropy|conditional]] differential entropy, and [[Kullback–Leibler divergence|relative entropy]] are defined in a similar fashion. Unlike the discrete analog, the differential entropy has an offset that depends on the units used to measure <math>X</math>.<ref name="gibbs">{{cite book |last=Gibbs |first=Josiah Willard |authorlink=Josiah Willard Gibbs |title=[[Elementary Principles in Statistical Mechanics|Elementary Principles in Statistical Mechanics, developed with especial reference to the rational foundation of thermodynamics]] |year=1902 |publisher=Charles Scribner's Sons |location=New York}}</ref>{{rp|183–184}} For example, the differential entropy of a quantity measured in millimeters will be {{not a typo|log(1000)}} more than the same quantity measured in meters; a dimensionless quantity will have differential entropy of {{not a typo|log(1000)}} more than the same quantity divided by 1000.
As with its discrete analog, the units of differential entropy depend on the base of the [[logarithm]], which is usually 2 (i.e., the units are [[bit]]s). See [[logarithmic units]] for logarithms taken in different bases. Related concepts such as [[joint entropy|joint]], [[conditional entropy|conditional]] differential entropy, and [[Kullback–Leibler divergence|relative entropy]] are defined in a similar fashion. Unlike the discrete analog, the differential entropy has an offset that depends on the units used to measure <math>X</math>.<ref name="gibbs">{{cite book |last=Gibbs |first=Josiah Willard |authorlink=Josiah Willard Gibbs |title=[[Elementary Principles in Statistical Mechanics|Elementary Principles in Statistical Mechanics, developed with especial reference to the rational foundation of thermodynamics]] |year=1902 |publisher=Charles Scribner's Sons |location=New York}}</ref>{{rp|183–184}} For example, the differential entropy of a quantity measured in millimeters will be {{not a typo|log(1000)}} more than the same quantity measured in meters; a dimensionless quantity will have differential entropy of {{not a typo|log(1000)}} more than the same quantity divided by 1000.
As with its discrete analog, the units of differential entropy depend on the base of the logarithm, which is usually 2 (i.e., the units are bits). See logarithmic units for logarithms taken in different bases. Related concepts such as joint, conditional differential entropy, and relative entropy are defined in a similar fashion. Unlike the discrete analog, the differential entropy has an offset that depends on the units used to measure <math>X</math>. For example, the differential entropy of a quantity measured in millimeters will be more than the same quantity measured in meters; a dimensionless quantity will have differential entropy of more than the same quantity divided by 1000.
和它的离散类似物一样，微分熵的单位依赖于对数的底，通常是2(也就是说，单位是位)。请参阅对数单位的对数采取在不同的基地。相关的概念，如联合，条件微分熵，和相对熵，都是以类似的方式定义的。与离散模拟不同，微分熵的偏移量取决于用于测量 < math > x </math > 的单位。例如，以毫米为单位测量的量的微分熵将大于以米为单位测量的相同量; 一个无量纲量的微分熵将大于相同量除以1000。
One must take care in trying to apply properties of discrete entropy to differential entropy, since probability density functions can be greater than 1. For example, the [[Uniform distribution (continuous)|uniform distribution]] <math>\mathcal{U}(0,1/2)</math> has ''negative'' differential entropy
One must take care in trying to apply properties of discrete entropy to differential entropy, since probability density functions can be greater than 1. For example, the [[Uniform distribution (continuous)|uniform distribution]] <math>\mathcal{U}(0,1/2)</math> has ''negative'' differential entropy
One must take care in trying to apply properties of discrete entropy to differential entropy, since probability density functions can be greater than 1. For example, the uniform distribution <math>\mathcal{U}(0,1/2)</math> has negative differential entropy
因为概率密度函数可以大于1，所以在尝试将离散熵的性质应用于微分熵时必须小心谨慎。例如，均匀分布 < math > mathcal { u }(0,1/2) </math > 具有负微分熵
:<math>\int_0^\frac{1}{2} -2\log(2)\,dx=-\log(2)\,</math>.
:<math>\int_0^\frac{1}{2} -2\log(2)\,dx=-\log(2)\,</math>.
<math>\int_0^\frac{1}{2} -2\log(2)\,dx=-\log(2)\,</math>.
2 log (2) ，dx =-log (2) ，</math > .
Thus, differential entropy does not share all properties of discrete entropy.
Thus, differential entropy does not share all properties of discrete entropy.
Thus, differential entropy does not share all properties of discrete entropy.
因此，微分熵并不具有离散熵的所有属性。
Note that the continuous [[mutual information]] <math>I(X;Y)</math> has the distinction of retaining its fundamental significance as a measure of discrete information since it is actually the limit of the discrete mutual information of ''partitions'' of <math>X</math> and <math>Y</math> as these partitions become finer and finer. Thus it is invariant under non-linear [[homeomorphisms]] (continuous and uniquely invertible maps), <ref>{{cite journal
Note that the continuous [[mutual information]] <math>I(X;Y)</math> has the distinction of retaining its fundamental significance as a measure of discrete information since it is actually the limit of the discrete mutual information of ''partitions'' of <math>X</math> and <math>Y</math> as these partitions become finer and finer. Thus it is invariant under non-linear [[homeomorphisms]] (continuous and uniquely invertible maps), <ref>{{cite journal
<math> 0 \leq D_{KL}(f || g) = \int_{-\infty}^\infty f(x) \log \left( \frac{f(x)}{g(x)} \right) dx = -h(f) - \int_{-\infty}^\infty f(x)\log(g(x)) dx.</math>
Note that the continuous mutual information <math>I(X;Y)</math> has the distinction of retaining its fundamental significance as a measure of discrete information since it is actually the limit of the discrete mutual information of partitions of <math>X</math> and <math>Y</math> as these partitions become finer and finer. Thus it is invariant under non-linear homeomorphisms (continuous and uniquely invertible maps), including linear transformations of <math>X</math> and <math>Y</math>, and still represents the amount of discrete information that can be transmitted over a channel that admits a continuous space of values.
请注意，连续互信息 i (x; y) </math > 的区别在于，作为离散信息的度量，它保留了其基本意义，因为它实际上是 < math > x </math > 和 < math > > y </math > 随着这些分区变得越来越精细，分区间的离散互信息的极限。因此它在非线性同胚(连续且唯一可逆的映射)下是不变的，包括 < math > x </math > 和 < math > y </math > 的线性变换，并且仍然表示可以通过允许连续空间值的通道传输的离散信息量。
| first = Alexander
| first = Alexander
| last = Kraskov
| last = Kraskov
For the direct analogue of discrete entropy extended to the continuous space, see limiting density of discrete points.
对于连续空间的离散熵的直接模拟，请参阅离散点的极限密度。
|author2=Stögbauer, Grassberger
|author2=Stögbauer, Grassberger
| year = 2004
| year = 2004
| title = Estimating mutual information
| title = Estimating mutual information
| journal = [[Physical Review E]]
| journal = [[Physical Review E]]
| volume = 60
| volume = 60
| pages = 066138
| pages = 066138
{| class="wikitable" style="background:white"
{ | class = “ wikitable” style = “ background: white”
| doi =10.1103/PhysRevE.69.066138
| doi =10.1103/PhysRevE.69.066138
|+ Table of differential entropies
| + 微分熵表
|arxiv = cond-mat/0305641 |bibcode = 2004PhRvE..69f6138K }}</ref> including linear <ref name = Reza>{{ cite book | title = An Introduction to Information Theory | author = Fazlollah M. Reza | publisher = Dover Publications, Inc., New York | origyear = 1961| year = 1994 | isbn = 0-486-68210-2 | url = https://books.google.com/books?id=RtzpRAiX6OgC&pg=PA8&dq=intitle:%22An+Introduction+to+Information+Theory%22++%22entropy+of+a+simple+source%22&as_brr=0&ei=zP79Ro7UBovqoQK4g_nCCw&sig=j3lPgyYrC3-bvn1Td42TZgTzj0Q }}</ref> transformations of <math>X</math> and <math>Y</math>, and still represents the amount of discrete information that can be transmitted over a channel that admits a continuous space of values.
|arxiv = cond-mat/0305641 |bibcode = 2004PhRvE..69f6138K }}</ref> including linear <ref name = Reza>{{ cite book | title = An Introduction to Information Theory | author = Fazlollah M. Reza | publisher = Dover Publications, Inc., New York | origyear = 1961| year = 1994 | isbn = 0-486-68210-2 | url = https://books.google.com/books?id=RtzpRAiX6OgC&pg=PA8&dq=intitle:%22An+Introduction+to+Information+Theory%22++%22entropy+of+a+simple+source%22&as_brr=0&ei=zP79Ro7UBovqoQK4g_nCCw&sig=j3lPgyYrC3-bvn1Td42TZgTzj0Q }}</ref> transformations of <math>X</math> and <math>Y</math>, and still represents the amount of discrete information that can be transmitted over a channel that admits a continuous space of values.
|-
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! Distribution Name !! Probability density function (pdf) !! Entropy in nats || Support
!发行名称! ！概率密度函数(pdf)！Nats 中的熵 | 支持
For the direct analogue of discrete entropy extended to the continuous space, see [[limiting density of discrete points]].
For the direct analogue of discrete entropy extended to the continuous space, see [[limiting density of discrete points]].
|-
|-
| Uniform || <math>f(x) = \frac{1}{b-a}</math> || <math>\ln(b - a) \,</math> ||<math>[a,b]\,</math>
<math>g(x)=\frac{1}{\sqrt{2\pi\sigma^2}}e^{-\frac{(x-\mu)^2}{2\sigma^2}}</math>
| 统一 | | < 数学 > f (x) = frac {1}{ b-a } </math > | < math > ln (b-a) ，</math > | < math > [ a，b ] ，</math >
==Properties of differential entropy==
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===Proof===
* For probability densities <math>f</math> and <math>g</math>, the [[Kullback–Leibler divergence]] <math>D_{KL}(f || g)</math> is greater than or equal to 0 with equality only if <math>f=g</math> [[almost everywhere]]. Similarly, for two random variables <math>X</math> and <math>Y</math>, <math>I(X;Y) \ge 0</math> and <math>h(X|Y) \le h(X)</math> with equality [[if and only if]] <math>X</math> and <math>Y</math> are [[Statistical independence|independent]].
| < math > h _ e (x) ，</math >
| Normal || <math>f(x) = \frac{1}{\sqrt{2\pi\sigma^2}} \exp\left(-\frac{(x-\mu)^2}{2\sigma^2}\right)</math> || <math>\ln\left(\sigma\sqrt{2\,\pi\,e}\right) </math>||<math>(-\infty,\infty)\,</math>
| <math>=-\int_0^\infty \lambda e^{-\lambda x} \log (\lambda e^{-\lambda x})\,dx</math>
| < math > =-int _ 0 ^ infty lambda e ^ {-lambda x } log (lambda e ^ {-lambda x }) ，dx </math >
| 正常 | | < math > f (x) = frac {1}{2 pi sigma ^ 2} exp left (- frac {(x-mu) ^ 2}{2 sigma ^ 2} right) </math > | < math > 左(sigma sqrt {2，pi，e } right) </math | < math > </math > (- infty，infty) ，</math >
* The chain rule for differential entropy holds as in the discrete case<ref name="cover_thomas" />{{rp|253}}
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::<math>h(X_1, \ldots, X_n) = \sum_{i=1}^{n} h(X_i|X_1, \ldots, X_{i-1}) \leq \sum_{i=1}^{n} h(X_i)</math>.
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| Exponential || <math>f(x) = \lambda \exp\left(-\lambda x\right)</math> || <math>1 - \ln \lambda \, </math>||<math>[0,\infty)\,</math>
指数 | | < math > f (x) = lambda exp left (- lambda x right) </math > | < math > 1-ln lambda，</math > | < math > [0，infty ] ，</math >
* Differential entropy is translation invariant, i.e. for a constant <math>c</math>.<ref name="cover_thomas" />{{rp|253}}
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:<math>\begin{align}
::<math>h(X+c) = h(X)</math>
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| Rayleigh || <math>f(x) = \frac{x}{\sigma^2} \exp\left(-\frac{x^2}{2\sigma^2}\right)</math> || <math>1 + \ln \frac{\sigma}{\sqrt{2}} + \frac{\gamma_E}{2}</math>||<math>[0,\infty)\,</math>
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| Rayleigh | | < math > f (x) = frac { x }{ sigma ^ 2} exp left (- frac { x ^ 2}{2 sigma ^ 2} right) </math > | < math > | < math > 1 + ln frac { sigma }{ sqrt {2}} + frac { e }{2} </math > | < math > [0，infty ] ，</math >
* Differential entropy is in general not invariant under arbitrary invertible maps.
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:: In particular, for a constant <math>a</math>
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| Beta || <math>f(x) = \frac{x^{\alpha-1}(1-x)^{\beta-1}}{B(\alpha,\beta)}</math> for <math>0 \leq x \leq 1</math> || <math> \ln B(\alpha,\beta) - (\alpha-1)[\psi(\alpha) - \psi(\alpha +\beta)]\,</math><br /><math>- (\beta-1)[\psi(\beta) - \psi(\alpha + \beta)] \, </math>||<math>[0,1]\,</math>
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| <math>= -\log\lambda + 1\,.</math>
| Beta| | < math > f (x) = frac { x ^ { alpha-1}(1-x) ^ { beta-1}{ b (alpha，beta)}{ math > for < math > 0 leq x leq 1 </math > | | < math > ln b (alpha，beta)-(alpha-1)[ psi (alpha)-psi (alpha + beta)] ，</math > < br/> < math >-(beta-1)[ psi (beta)-psi (alpha + beta)] ，</math > | | < math > [0,1] ，</math >
:::<math>h(aX) = h(X)+ \log |a|</math>
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:: For a vector valued random variable <math>\mathbf{X}</math> and an invertible (square) [[matrix (mathematics)|matrix]] <math>\mathbf{A}</math>
| Cauchy || <math>f(x) = \frac{\gamma}{\pi} \frac{1}{\gamma^2 + x^2}</math> || <math>\ln(4\pi\gamma) \, </math>||<math>(-\infty,\infty)\,</math>
| Cauchy || <math>f(x) = \frac{\gamma}{\pi} \frac{1}{\gamma^2 + x^2}</math> || <math>\ln(4\pi\gamma) \, </math>||<math>(-\infty,\infty)\,</math>
| Cauchy | | < math > f (x) = frac { gamma }{ pi }{ pi ^ 2 + x ^ 2} </math > | < math > ln (4pi gamma) ，</math > | < math > (- infty，infty) ，</math >
| Cauchy | | < math > f (x) = frac { gamma }{ pi }{ pi ^ 2 + x ^ 2} </math > | < math > ln (4pi gamma) ，</math > | < math > (- infty，infty) ，</math >
:::<math>h(\mathbf{A}\mathbf{X})=h(\mathbf{X})+\log \left( |\det \mathbf{A}| \right)</math><ref name="cover_thomas" />{{rp|253}}
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* In general, for a transformation from a random vector to another random vector with same dimension <math>\mathbf{Y}=m \left(\mathbf{X}\right)</math>, the corresponding entropies are related via
| Chi || <math>f(x) = \frac{2}{2^{k/2} \Gamma(k/2)} x^{k-1} \exp\left(-\frac{x^2}{2}\right)</math> || <math>\ln{\frac{\Gamma(k/2)}{\sqrt{2}}} - \frac{k-1}{2} \psi\left(\frac{k}{2}\right) + \frac{k}{2}</math>||<math>[0,\infty)\,</math>
| Chi || <math>f(x) = \frac{2}{2^{k/2} \Gamma(k/2)} x^{k-1} \exp\left(-\frac{x^2}{2}\right)</math> || <math>\ln{\frac{\Gamma(k/2)}{\sqrt{2}}} - \frac{k-1}{2} \psi\left(\frac{k}{2}\right) + \frac{k}{2}</math>||<math>[0,\infty)\,</math>
| Chi | | < math > f (x) = frac {2}{2 ^ { k/2} Gamma (k/2)}} x ^ { k-1} exp left (- frac { x ^ 2}{2}右) </math > | < math > ln { frac {(k/2)}}}{2}}}-frac {2} psi (frac { k }{2}右) + frac {2} </math > | | math > [0，infty) ，</math >
| Chi | | < math > f (x) = frac {2}{2 ^ { k/2} Gamma (k/2)}} x ^ { k-1} exp left (- frac { x ^ 2}{2}{右) </math > | < math > ln { frac {(k/2)}}{2}}}}-frac {2} psi (frac { k }{2}右) + frac {2}{2} </math > | | math > [0，infty) ，</math >
::<math>h(\mathbf{Y}) \leq h(\mathbf{X}) + \int f(x) \log \left\vert \frac{\partial m}{\partial x} \right\vert dx</math>
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:where <math>\left\vert \frac{\partial m}{\partial x} \right\vert</math> is the [[Jacobian matrix and determinant|Jacobian]] of the transformation <math>m</math>.<ref>{{cite web |title=proof of upper bound on differential entropy of f(X) |work=[[Stack Exchange]] |date=April 16, 2016 |url=https://math.stackexchange.com/q/1745670 }}</ref> The above inequality becomes an equality if the transform is a bijection. Furthermore, when <math>m</math> is a rigid rotation, translation, or combination thereof, the Jacobian determinant is always 1, and <math>h(Y)=h(X)</math>.
| Chi-squared || <math>f(x) = \frac{1}{2^{k/2} \Gamma(k/2)} x^{\frac{k}{2}\!-\!1} \exp\left(-\frac{x}{2}\right)</math> || <math>\ln 2\Gamma\left(\frac{k}{2}\right) - \left(1 - \frac{k}{2}\right)\psi\left(\frac{k}{2}\right) + \frac{k}{2}</math>||<math>[0,\infty)\,</math>
| Chi-squared || <math>f(x) = \frac{1}{2^{k/2} \Gamma(k/2)} x^{\frac{k}{2}\!-\!1} \exp\left(-\frac{x}{2}\right)</math> || <math>\ln 2\Gamma\left(\frac{k}{2}\right) - \left(1 - \frac{k}{2}\right)\psi\left(\frac{k}{2}\right) + \frac{k}{2}</math>||<math>[0,\infty)\,</math>
| Chi-squared | < math > f (x) = frac {1}{2 ^ { k/2} Gamma (k/2)} x ^ { frac { k }{2} !-! 1} exp left (- frac { x }{2}右) </math > | < math > | < math > ln 2 Gamma left (frac { k }{2}右)-left (1-frac { k }{2}右)左(frac { k }2}右) + c { k {2}{ infmath | < < math > [0，fraty) ，</math >
| Chi-squared | < math > f (x) = frac {1}{2 ^ { k/2} Gamma (k/2)} x ^ { frac { k }{2} !-! 1} exp left (- frac { x }{2}右) </math > | < math > | < math > ln 2 Gamma left (frac { k }{2}右)-left (1-frac { k }{2}右)左(frac { k }2}右) + c { k {2}{ infmath | < < math > [0，fraty) ，</math >
* If a random vector <math>X \in \mathbb{R}^n</math> has mean zero and [[covariance]] matrix <math>K</math>, <math>h(\mathbf{X}) \leq \frac{1}{2} \log(\det{2 \pi e K}) = \frac{1}{2} \log[(2\pi e)^n \det{K}]</math> with equality if and only if <math>X</math> is [[Multivariate normal distribution#Joint normality|jointly gaussian]] (see [[#Maximization in the normal distribution|below]]).<ref name="cover_thomas" />{{rp|254}}
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| Erlang || <math>f(x) = \frac{\lambda^k}{(k-1)!} x^{k-1} \exp(-\lambda x)</math> || <math>(1-k)\psi(k) + \ln \frac{\Gamma(k)}{\lambda} + k</math>||<math>[0,\infty)\,</math>
| Erlang || <math>f(x) = \frac{\lambda^k}{(k-1)!} x^{k-1} \exp(-\lambda x)</math> || <math>(1-k)\psi(k) + \ln \frac{\Gamma(k)}{\lambda} + k</math>||<math>[0,\infty)\,</math>
| Erlang | | < math > f (x) = frac { lambda ^ k }{(k-1) ! }X ^ { k-1} exp (- lambda x) </math > | < math > (1-k) psi (k) + ln frac { Gamma (k)}{ lambda } + k </math > | < math > [0，infty ] ，</math >
| Erlang | | < math > f (x) = frac { lambda ^ k }{(k-1) ! }X ^ { k-1} exp (- lambda x) </math > | < math > (1-k) psi (k) + ln frac { Gamma (k)}{ lambda } + k </math > | < math > [0，infty ] ，</math >
However, differential entropy does not have other desirable properties:
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* It is not invariant under [[change of variables]], and is therefore most useful with dimensionless variables.
| F || <math>f(x) = \frac{n_1^{\frac{n_1}{2}} n_2^{\frac{n_2}{2}}}{B(\frac{n_1}{2},\frac{n_2}{2})} \frac{x^{\frac{n_1}{2} - 1}}{(n_2 + n_1 x)^{\frac{n_1 + n2}{2}}}</math> || <math>\ln \frac{n_1}{n_2} B\left(\frac{n_1}{2},\frac{n_2}{2}\right) + \left(1 - \frac{n_1}{2}\right) \psi\left(\frac{n_1}{2}\right) -</math><br /><math>\left(1 + \frac{n_2}{2}\right)\psi\left(\frac{n_2}{2}\right) + \frac{n_1 + n_2}{2} \psi\left(\frac{n_1\!+\!n_2}{2}\right)</math>||<math>[0,\infty)\,</math>
| F || <math>f(x) = \frac{n_1^{\frac{n_1}{2}} n_2^{\frac{n_2}{2}}}{B(\frac{n_1}{2},\frac{n_2}{2})} \frac{x^{\frac{n_1}{2} - 1}}{(n_2 + n_1 x)^{\frac{n_1 + n2}{2}}}</math> || <math>\ln \frac{n_1}{n_2} B\left(\frac{n_1}{2},\frac{n_2}{2}\right) + \left(1 - \frac{n_1}{2}\right) \psi\left(\frac{n_1}{2}\right) -</math><br /><math>\left(1 + \frac{n_2}{2}\right)\psi\left(\frac{n_2}{2}\right) + \frac{n_1 + n_2}{2} \psi\left(\frac{n_1\!+\!n_2}{2}\right)</math>||<math>[0,\infty)\,</math>
我们会找到你的| | < math > f (x) = frac{ n _ 1 ^ { frac { n _ 1}{2}{ frac { n _ 2}{2}}{ b (frac { n _ 1}{2} ，frac { n _ 2}{2}}}}}} frac { x ^ { frac { n _ 1}{2}-1}{(n _ 2 + n _ 1 x) ^ { frac { n _ 1 + n _ 2}{2}}{2}{2}} </} </math > | | | (frac { n _ 1}{ n _ 2} b left (frac { n _ 1}{2} ,2}{2}{2}{2}{2}{2}{1}{2}{2}{2}{2}{2}{2}{2}{2}{2}{2}{1}{2}{2}{2}{2}{2}{2}{2}{2}{2}{2}{2}{2}{2}{2}{2}{2}{2}{2} psi 左(frac { n _ 1！+\！[0，infty) ，</math > | < math >
我们会找到你的| | < math > f (x) = frac{ n _ 1 ^ { frac { n _ 1}{2}{ frac { n _ 2}{2}}{ b (frac { n _ 1}{2} ，frac { n _ 2}{2}}}}}} frac { x ^ { frac { n _ 1}{2}-1}{(n _ 2 + n _ 1 x) ^ { frac { n _ 1 + n _ 2}{2}}{2}{2}} </} </math > | | | (frac { n _ 1}{ n _ 2} b left (frac { n _ 1}{2} ,2}{2}{2}{2}{2}{2}{1}{2}{2}{2}{2}{2}{2}{2}{2}{2}{2}{1}{2}{2}{2}{2}{2}{2}{2}{2}{2}{2}{2}{2}{2}{2}{2}{2}{2}{2} psi 左(frac { n _ 1！+\！[0，infty) ，</math > | < math >
* It can be negative.
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A modification of differential entropy that addresses these drawbacks is the '''relative information entropy''', also known as the Kullback–Leibler divergence, which includes an [[invariant measure]] factor (see [[limiting density of discrete points]]).
| Gamma || <math>f(x) = \frac{x^{k - 1} \exp(-\frac{x}{\theta})}{\theta^k \Gamma(k)}</math> || <math>\ln(\theta \Gamma(k)) + (1 - k)\psi(k) + k \, </math>||<math>[0,\infty)\,</math>
| Gamma || <math>f(x) = \frac{x^{k - 1} \exp(-\frac{x}{\theta})}{\theta^k \Gamma(k)}</math> || <math>\ln(\theta \Gamma(k)) + (1 - k)\psi(k) + k \, </math>||<math>[0,\infty)\,</math>
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==Example: Exponential distribution==
==Maximization in the normal distribution==
| Laplace || <math>f(x) = \frac{1}{2b} \exp\left(-\frac{|x - \mu|}{b}\right)</math> || <math>1 + \ln(2b) \, </math>||<math>(-\infty,\infty)\,</math>
| Laplace || <math>f(x) = \frac{1}{2b} \exp\left(-\frac{|x - \mu|}{b}\right)</math> || <math>1 + \ln(2b) \, </math>||<math>(-\infty,\infty)\,</math>
| Laplace | | < math > f (x) = frac {1}{2b } exp left (- frac { | x-mu | }{ b } right) </math > | < math > 1 + ln (2b) ，</math > | < math > (- infty，infty) ，</math >
| Laplace | | < math > f (x) = frac {1}{2b } exp left (- frac { | x-mu | }{ b } right) </math > | < math > 1 + ln (2b) ，</math > | < math > (- infty，infty) ，</math >
===Theorem===
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With a [[normal distribution]], differential entropy is maximized for a given variance. A Gaussian random variable has the largest entropy amongst all random variables of equal variance, or, alternatively, the maximum entropy distribution under constraints of mean and variance is the Gaussian.<ref name="cover_thomas" />{{rp|255}}
| Logistic || <math>f(x) = \frac{e^{-x}}{(1 + e^{-x})^2}</math> || <math>2 \, </math>||<math>(-\infty,\infty)\,</math>
| Logistic || <math>f(x) = \frac{e^{-x}}{(1 + e^{-x})^2}</math> || <math>2 \, </math>||<math>(-\infty,\infty)\,</math>
| Logistic | | < math > f (x) = frac { e ^ {-x }{(1 + e ^ {-x }) ^ 2} </math > | < math > 2，</math > | < math > (- infty，infty) ，</math >
| Logistic | | < math > f (x) = frac { e ^ {-x }{(1 + e ^ {-x }) ^ 2} </math > | < math > 2，</math > | < math > (- infty，infty) ，</math >
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===Proof===
| Lognormal || <math>f(x) = \frac{1}{\sigma x \sqrt{2\pi}} \exp\left(-\frac{(\ln x - \mu)^2}{2\sigma^2}\right)</math> || <math>\mu + \frac{1}{2} \ln(2\pi e \sigma^2)</math>||<math>[0,\infty)\,</math>
| Lognormal || <math>f(x) = \frac{1}{\sigma x \sqrt{2\pi}} \exp\left(-\frac{(\ln x - \mu)^2}{2\sigma^2}\right)</math> || <math>\mu + \frac{1}{2} \ln(2\pi e \sigma^2)</math>||<math>[0,\infty)\,</math>
| Lognormal | < math > f (x) = frac {1}{ sigma x sqrt {2 pi } exp left (- frac {(ln x-mu) ^ 2}{2 sigma ^ 2} right) </math > | < math > mu + frac {1}{2} ln (2 pi e sigma ^ 2) </math > | < math > [0，infty) ，</math >
| Lognormal | < math > f (x) = frac {1}{ sigma x sqrt {2 pi } exp left (- frac {(ln x-mu) ^ 2}{2 sigma ^ 2} right) </math > | < math > mu + frac {1}{2} ln (2 pi e sigma ^ 2) </math > | < math > [0，infty) ，</math >
Let <math>g(x)</math> be a [[Normal distribution|Gaussian]] [[Probability density function|PDF]] with mean μ and variance <math>\sigma^2</math> and <math>f(x)</math> an arbitrary [[Probability density function|PDF]] with the same variance. Since differential entropy is translation invariant we can assume that <math>f(x)</math> has the same mean of <math>\mu</math> as <math>g(x)</math>.
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| Maxwell–Boltzmann || <math>f(x) = \frac{1}{a^3}\sqrt{\frac{2}{\pi}}\,x^{2}\exp\left(-\frac{x^2}{2a^2}\right)</math> || <math>\ln(a\sqrt{2\pi})+\gamma_E-\frac{1}{2}</math>||<math>[0,\infty)\,</math>
| Maxwell–Boltzmann || <math>f(x) = \frac{1}{a^3}\sqrt{\frac{2}{\pi}}\,x^{2}\exp\left(-\frac{x^2}{2a^2}\right)</math> || <math>\ln(a\sqrt{2\pi})+\gamma_E-\frac{1}{2}</math>||<math>[0,\infty)\,</math>
| Maxwell-Boltzmann | | < math > f (x) = frac {1}{ a ^ 3}{ frac {2}{ pi } ，x ^ {2} exp left (- frac { x ^ 2}{2a ^ 2}右) </math > | < math > ln (a sqrt {2 pi }) + e-frac {1} </math > | | math < 0，infty) ，</math >
| Maxwell-Boltzmann | | < math > f (x) = frac {1}{ a ^ 3}{ frac {2}{ pi } ，x ^ {2} exp left (- frac { x ^ 2}{2a ^ 2}右) </math > | < math > ln (a sqrt {2 pi }) + e-frac {1} </math > | | math < 0，infty) ，</math >
Consider the [[Kullback–Leibler divergence]] between the two distributions
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:<math> 0 \leq D_{KL}(f || g) = \int_{-\infty}^\infty f(x) \log \left( \frac{f(x)}{g(x)} \right) dx = -h(f) - \int_{-\infty}^\infty f(x)\log(g(x)) dx.</math>
| Generalized normal || <math>f(x) = \frac{2 \beta^{\frac{\alpha}{2}}}{\Gamma(\frac{\alpha}{2})} x^{\alpha - 1} \exp(-\beta x^2)</math> || <math>\ln{\frac{\Gamma(\alpha/2)}{2\beta^{\frac{1}{2}}}} - \frac{\alpha - 1}{2} \psi\left(\frac{\alpha}{2}\right) + \frac{\alpha}{2}</math>||<math>(-\infty,\infty)\,</math>
| Generalized normal || <math>f(x) = \frac{2 \beta^{\frac{\alpha}{2}}}{\Gamma(\frac{\alpha}{2})} x^{\alpha - 1} \exp(-\beta x^2)</math> || <math>\ln{\frac{\Gamma(\alpha/2)}{2\beta^{\frac{1}{2}}}} - \frac{\alpha - 1}{2} \psi\left(\frac{\alpha}{2}\right) + \frac{\alpha}{2}</math>||<math>(-\infty,\infty)\,</math>
| 广义正态| | < math > f (x) = frac{2 beta ^ { frac { alpha }{2}{ Gamma (frac { alpha }{2})} x ^ { alpha-1} exp (- beta x ^ 2) </math > | | < math > ln { frac { Gamma (alpha/2)}{2 beta ^ { frac {1}{2}}}}}-frac { alpha-1}{2} psi left (frac { alpha }{2} right) + frac { alpha }{2}}{2}| | < math > (- infty，infty) ，</math >
| 广义正态| | < math > f (x) = frac{2 beta ^ { frac { alpha }{2}{ Gamma (frac { alpha }{2})} x ^ { alpha-1} exp (- beta x ^ 2) </math > | | < math > ln { frac { Gamma (alpha/2)}{2 beta ^ { frac {1}{2}}}}-frac { alpha-1}{2} psi left (frac { alpha }{2} right) + frac { alpha }{2}}{2} </math > | | < math > (- infty，infty) ，</math >
Now note that
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:<math>\begin{align}
| Pareto || <math>f(x) = \frac{\alpha x_m^\alpha}{x^{\alpha+1}}</math> || <math>\ln \frac{x_m}{\alpha} + 1 + \frac{1}{\alpha}</math>||<math>[x_m,\infty)\,</math>
| Pareto || <math>f(x) = \frac{\alpha x_m^\alpha}{x^{\alpha+1}}</math> || <math>\ln \frac{x_m}{\alpha} + 1 + \frac{1}{\alpha}</math>||<math>[x_m,\infty)\,</math>
| Pareto | < math > f (x) = frac { alpha x _ m ^ alpha }{ x ^ { alpha + 1}} </math > | < math > ln frac { x _ m }{ alpha } + 1 + frac {1}{ alpha } </math > | < math > [ x _ m，infty ] ，</math >
| Pareto | < math > f (x) = frac { alpha x _ m ^ alpha }{ x ^ { alpha + 1}} </math > | < math > ln frac { x _ m }{ alpha } + 1 + frac {1}{ alpha } </math > | < math > [ x _ m，infty ] ，</math >
\int_{-\infty}^\infty f(x)\log(g(x)) dx &= \int_{-\infty}^\infty f(x)\log\left( \frac{1}{\sqrt{2\pi\sigma^2}}e^{-\frac{(x-\mu)^2}{2\sigma^2}}\right) dx \\
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&= \int_{-\infty}^\infty f(x) \log\frac{1}{\sqrt{2\pi\sigma^2}} dx + \log(e)\int_{-\infty}^\infty f(x)\left( -\frac{(x-\mu)^2}{2\sigma^2}\right) dx \\
| Student's t || <math>f(x) = \frac{(1 + x^2/\nu)^{-\frac{\nu+1}{2}}}{\sqrt{\nu}B(\frac{1}{2},\frac{\nu}{2})}</math> || <math>\frac{\nu\!+\!1}{2}\left(\psi\left(\frac{\nu\!+\!1}{2}\right)\!-\!\psi\left(\frac{\nu}{2}\right)\right)\!+\!\ln \sqrt{\nu} B\left(\frac{1}{2},\frac{\nu}{2}\right)</math>||<math>(-\infty,\infty)\,</math>
| Student's t || <math>f(x) = \frac{(1 + x^2/\nu)^{-\frac{\nu+1}{2}}}{\sqrt{\nu}B(\frac{1}{2},\frac{\nu}{2})}</math> || <math>\frac{\nu\!+\!1}{2}\left(\psi\left(\frac{\nu\!+\!1}{2}\right)\!-\!\psi\left(\frac{\nu}{2}\right)\right)\!+\!\ln \sqrt{\nu} B\left(\frac{1}{2},\frac{\nu}{2}\right)</math>||<math>(-\infty,\infty)\,</math>
| Student’s t | < math > f (x) = frac {(1 + x ^ 2/nu) ^ {-frac { nu + 1}{2}}{{ sqrt { nu } b (frac {1}{2} ，frac { nu }{2})} </math | | | < math > frac { nu! + ! 1}{2}右) !-! 左(psi (frac { nu! + 1}{2}右) !-! 左(frac { nu }{2右) ! + ! { nu }{ n 左(frac {2}右)
| Student’s t | < math > f (x) = frac {(1 + x ^ 2/nu) ^ {-frac { nu + 1}{2}}{{ sqrt { nu } b (frac {1}{2} ，frac { nu }{2})} </math | | | < math > frac { nu! + ! 1}{2}(左(frac { nu! + 1}{2}右) !-! 左(frac { nu! + 1}{2}右) !-! 左(frac { nu }{2右) ! + ! { nu }{ b 左(frac {2，c {2}{2}{右)
&= -\tfrac{1}{2}\log(2\pi\sigma^2) - \log(e)\frac{\sigma^2}{2\sigma^2} \\
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&= -\tfrac{1}{2}\left(\log(2\pi\sigma^2) + \log(e)\right) \\
| Triangular || <math> f(x) = \begin{cases}
| Triangular || <math> f(x) = \begin{cases}
| 三角形 | | < math > f (x) = begin { cases }
| 三角形 | | < math > f (x) = begin { cases }
&= -\tfrac{1}{2}\log(2\pi e \sigma^2) \\
\frac{2(x-a)}{(b-a)(c-a)} & \mathrm{for\ } a \le x \leq c, \\[4pt]
\frac{2(x-a)}{(b-a)(c-a)} & \mathrm{for\ } a \le x \leq c, \\[4pt]
Frac {2(x-a)}{(b-a)(c-a)} & mathrm { for } a le x leq c，[4 pt ]
Frac {2(x-a)}{(b-a)(c-a)} & mathrm { for } a le x leq c，[4 pt ]
&= -h(g)
\frac{2(b-x)}{(b-a)(b-c)} & \mathrm{for\ } c < x \le b, \\[4pt]
\frac{2(b-x)}{(b-a)(b-c)} & \mathrm{for\ } c < x \le b, \\[4pt]
Frac {2(b-x)}{(b-a)(b-c)} & mathrm { for } c < x le b，[4 pt ]
Frac {2(b-x)}{(b-a)(b-c)} & mathrm { for } c < x le b，[4 pt ]
\end{align}</math>
\end{cases}</math> || <math>\frac{1}{2} + \ln \frac{b-a}{2}</math>||<math>[0,1]\,</math>
\end{cases}</math> || <math>\frac{1}{2} + \ln \frac{b-a}{2}</math>||<math>[0,1]\,</math>
结束{ cases } </math > | | < math > frac {1}{2} + ln frac { b-a }{2} </math > | < math > [0,1] ，</math >
结束{ cases } </math > | | < math > frac {1}{2} + ln frac { b-a }{2} </math > | < math > [0,1] ，</math >
because the result does not depend on <math>f(x)</math> other than through the variance. Combining the two results yields
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:<math> h(g) - h(f) \geq 0 \!</math>
| Weibull || <math>f(x) = \frac{k}{\lambda^k} x^{k-1} \exp\left(-\frac{x^k}{\lambda^k}\right)</math> || <math>\frac{(k-1)\gamma_E}{k} + \ln \frac{\lambda}{k} + 1</math>||<math>[0,\infty)\,</math>
| Weibull || <math>f(x) = \frac{k}{\lambda^k} x^{k-1} \exp\left(-\frac{x^k}{\lambda^k}\right)</math> || <math>\frac{(k-1)\gamma_E}{k} + \ln \frac{\lambda}{k} + 1</math>||<math>[0,\infty)\,</math>
| Weibull | | < math > f (x) = frac { k }{ lambda ^ k } x ^ { k-1} exp left (- frac { x ^ k }{ lambda ^ k } right) </math > | < math > | < math > frac {(k-1) gamma _ e }{ k } + ln frac { lambda }{ k } + 1 </math > | < math > [0，infty) ，</math >
| Weibull | | < math > f (x) = frac { k }{ lambda ^ k } x ^ { k-1} exp left (- frac { x ^ k }{ lambda ^ k } right) </math > | < math > | < math > frac {(k-1) gamma _ e }{ k } + ln frac { lambda }{ k } + 1 </math > | < math > [0，infty) ，</math >
with equality when <math>f(x)=g(x)</math> following from the properties of Kullback–Leibler divergence.
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| Multivariate normal || <math>
| Multivariate normal || <math>
多元正态 | | < 数学 >
多元正态 | | < 数学 >
===Alternative proof===
f_X(\vec{x}) =</math><br /><math> \frac{\exp \left( -\frac{1}{2} ( \vec{x} - \vec{\mu})^\top \Sigma^{-1}\cdot(\vec{x} - \vec{\mu}) \right)} {(2\pi)^{N/2} \left|\Sigma\right|^{1/2}}</math> || <math>\frac{1}{2}\ln\{(2\pi e)^{N} \det(\Sigma)\}</math>||<math>\mathbb{R}^N</math>
F _ x (vec { x }) = </math > < br/> < math > frac { exp left (- frac {1}{2}(vec { x }-vec { mu }) ^ top Sigma ^ {-1} cdot (vec { x }-vec { mu }) right)}{(2 pi) ^ { N/2}左 Sigma | right | ^ {1/2} < | < math > | < < | < math > frac {1}{ ln (2 pi e){{ n } | math < | | | > 数学 < bb >
This result may also be demonstrated using the [[variational calculus]]. A Lagrangian function with two [[Lagrangian multiplier]]s may be defined as:
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:<math>\operatorname{E}[(X - \widehat{X})^2] \ge \frac{1}{2\pi e}e^{2h(X)}</math>
:<math>L=\int_{-\infty}^\infty g(x)\ln(g(x))\,dx-\lambda_0\left(1-\int_{-\infty}^\infty g(x)\,dx\right)-\lambda\left(\sigma^2-\int_{-\infty}^\infty g(x)(x-\mu)^2\,dx\right)</math>
Many of the differential entropies are from.
Many of the differential entropies are from.
许多熵的差异来自于。
许多熵的差异来自于。
where ''g(x)'' is some function with mean μ. When the entropy of ''g(x)'' is at a maximum and the constraint equations, which consist of the normalization condition <math>\left(1=\int_{-\infty}^\infty g(x)\,dx\right)</math> and the requirement of fixed variance <math>\left(\sigma^2=\int_{-\infty}^\infty g(x)(x-\mu)^2\,dx\right)</math>, are both satisfied, then a small variation δ''g''(''x'') about ''g(x)'' will produce a variation δ''L'' about ''L'' which is equal to zero:
==Differential entropies for various distributions==
:<math>0=\delta L=\int_{-\infty}^\infty \delta g(x)\left (\ln(g(x))+1+\lambda_0+\lambda(x-\mu)^2\right )\,dx</math>
As described above, differential entropy does not share all properties of discrete entropy. For example, the differential entropy can be negative; also it is not invariant under continuous coordinate transformations. Edwin Thompson Jaynes showed in fact that the expression above is not the correct limit of the expression for a finite set of probabilities.
如上所述，微分熵并不具有离散熵的所有属性。例如，微分熵可以是负的，也不是连续坐标变换下的不变量。埃德温·汤普森·杰尼斯表明，事实上，上面的表达式不是一个有限的概率集合表达式的正确极限。
Since this must hold for any small δ''g''(''x''), the term in brackets must be zero, and solving for ''g(x)'' yields:
A modification of differential entropy adds an invariant measure factor to correct this, (see limiting density of discrete points). If <math>m(x)</math> is further constrained to be a probability density, the resulting notion is called relative entropy in information theory:
一个修改的微分熵增加了一个不变测度因子来纠正这个错误。如果 < math > m (x) </math > 被进一步限制为概率密度，那么由此产生的概念在信息论中被称为相对熵:
:<math>g(x)=e^{-\lambda_0-1-\lambda(x-\mu)^2}</math>
<math>D(p||m) = \int p(x)\log\frac{p(x)}{m(x)}\,dx.</math>
< math > d (p | | | m) = int p (x) log frac { p (x)}{ m (x)} ，dx. </math >
Using the constraint equations to solve for λ<sub>0</sub> and λ yields the normal distribution:
The definition of differential entropy above can be obtained by partitioning the range of <math>X</math> into bins of length <math>h</math> with associated sample points <math>ih</math> within the bins, for <math>X</math> Riemann integrable. This gives a quantized version of <math>X</math>, defined by <math>X_h = ih</math> if <math>ih \le X \le (i+1)h</math>. Then the entropy of <math>X_h = ih</math> is
上面的微分熵的定义可以通过把 < math > x </math > 的范围划分到与样本点 < math > h </math > 相关的箱子里来得到，因为 < math > x </math > Riemann 可积。这给出了一个量化版本的 < math > x </math > ，定义为 < math > x _ h = ih </math > if < math > > ih le x le (i + 1) h </math > 。然后得到了系统的熵
:<math>g(x)=\frac{1}{\sqrt{2\pi\sigma^2}}e^{-\frac{(x-\mu)^2}{2\sigma^2}}</math>
<math>H_h=-\sum_i hf(ih)\log (f(ih)) - \sum hf(ih)\log(h).</math>
<math>H_h=-\sum_i hf(ih)\log (f(ih)) - \sum hf(ih)\log(h).</math>
(h)-sum hf (ih) log (f (ih)-sum hf (ih) log (h)
==Example: Exponential distribution==
The first term on the right approximates the differential entropy, while the second term is approximately <math>-\log(h)</math>. Note that this procedure suggests that the entropy in the discrete sense of a continuous random variable should be <math>\infty</math>.
The first term on the right approximates the differential entropy, while the second term is approximately <math>-\log(h)</math>. Note that this procedure suggests that the entropy in the discrete sense of a continuous random variable should be <math>\infty</math>.
右边的第一个术语近似于微分熵，而第二个术语近似于 math >-log (h) </math > 。请注意，这个过程表明，连续随机变量的离散意义上的熵应该是“数学”。
Let <math>X</math> be an [[exponential distribution|exponentially distributed]] random variable with parameter <math>\lambda</math>, that is, with probability density function
:<math>f(x) = \lambda e^{-\lambda x} \mbox{ for } x \geq 0.</math>
Its differential entropy is then
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| <math>h_e(X)\,</math>
| <math>=-\int_0^\infty \lambda e^{-\lambda x} \log (\lambda e^{-\lambda x})\,dx</math>
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| [[Rayleigh distribution|Rayleigh]] || <math>f(x) = \frac{x}{\sigma^2} \exp\left(-\frac{x^2}{2\sigma^2}\right)</math> || <math>1 + \ln \frac{\sigma}{\sqrt{2}} + \frac{\gamma_E}{2}</math>||<math>[0,\infty)\,</math>
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| <math>= -\left(\int_0^\infty (\log \lambda)\lambda e^{-\lambda x}\,dx + \int_0^\infty (-\lambda x) \lambda e^{-\lambda x}\,dx\right) </math>
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| [[Beta distribution|Beta]] || <math>f(x) = \frac{x^{\alpha-1}(1-x)^{\beta-1}}{B(\alpha,\beta)}</math> for <math>0 \leq x \leq 1</math> || <math> \ln B(\alpha,\beta) - (\alpha-1)[\psi(\alpha) - \psi(\alpha +\beta)]\,</math><br /><math>- (\beta-1)[\psi(\beta) - \psi(\alpha + \beta)] \, </math>||<math>[0,1]\,</math>
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Category:Entropy and information
Category:Entropy and information
类别: 熵和信息
类别: 熵和信息
| <math>= -\log \lambda \int_0^\infty f(x)\,dx + \lambda E[X]</math>
Category:Information theory
Category:Information theory