定义3.1 [KL-散度]: 给定取值集合均为 X 的两个离散随机变量 X 和X^ ,我们记二者的 KL-散度 (Kullback–Leibler divergence) 为 D(X‖X^) 或者D(pX‖pX^) ,定义为 D(X‖X^)=D(pX‖pX^):=EX(log2pX(X)pX^(X))=∑x∈XpX(x)log2pX(x)pX^(x)KL-散度也被称为相对熵 (Relative...
统计距离：https://en.wikipedia.org/wiki/Statistical_distance f - divergence：https://en.wikipedia.org/wiki/F-divergence：包括了 KL 散度的其余变形方式。Bregman distance：https://en.wikipedia.org/wiki/Bregman_divergence Jensen Shannon Divergence：https://en.wikipedia.org/wiki/Jensen%E2%80%93Shannon...
framework as the previous non-tight parallel-repetition theorem of Hstad et al-which relied on statistical distance to measure the distance between experiments-and show that it can be made tight (and further simplified) if instead relying on KL-divergence as the distance between the experiments. ...
In this post, you will discover how to calculate the divergence between probability distributions. After reading this post, you will know: Statistical distance is the general idea of calculating the difference between statistical objects like different probability distributions for a random variable. Kull...
KL散度(Kullback-Leibler divergence)概念:KL散度( Kullback-Leibler divergence)也被称为相对熵,是一种非对称度量方法,常用于度量两个概率分布之间的距离。KL散度也可以衡量两个随机分布之间的距离,两个随机分布的相似度越高的,它们的KL散度越小,当两个随机分布的差别增大时,它们的KL散度也会增大,因此KL散度可以用于...
1.KL散度 KL散度( Kullback–Leibler divergence) (最大类间散度) 是描述两个概率分布差异的一种测度。对于两个概率分布P、Q,二者越相似,KL散度越小。 KL散度的性质:P:真实分布,Q:P的拟合分布非负性:KL(P||Q)>=0,当P=Q时,KL(P||Q)=0;反身性:KL(P||P)=0 非对称性:D(P||Q) ≠ D(Q||...
this convex loss function is derived from a general distance metric (Kullback_Leibler divergence) for two probability distributions. See ://en.wikipedia.org/ wiki/ Kullback-Leibler_divergence –and especially the examples section there ://en.wikipedia.org/ wiki/ Divergence_...
(HPD)], we inferred that the common ancestor for the ST11K. pneumoniaechromosome was in 1891 (1864–1916, 95% HPD with a strict clock and coalescent constant model). Around 2000, a common ancestor of the prevalent ST11 strain in China emerged, followed by a divergence from a branch of ...
framework as the previous non-tight parallel-repetition theorem of Hastad et al-which relied on statistical distance to measure the distance between experiments-and show that it can be made tight (and further simplified) if instead relying on KL-divergence as the distance between the experiments....
二、 KL divergence 一些性质（非正式）证明 1. 非对称性\begin{align} D_{KL}(p||q) - D_{...