In information theory, the cross-entropy between two probability distributions p {\displaystyle p} and q {\displaystyle q} , over the same underlying...

In information theory, the entropy of a random variable quantifies the average level of uncertainty or information associated with the variable's potential...

The cross-entropy (CE) method is a Monte Carlo method for importance sampling and optimization. It is applicable to both combinatorial and continuous...

statistics, the Kullback–Leibler (KL) divergence (also called relative entropy and I-divergence), denoted D KL ( P ∥ Q ) {\displaystyle D_{\text{KL}}(P\parallel...

correlation for regression tasks or using information measures such as cross entropy for classification tasks. Theoretically, one can justify the diversity...

entropy states that the probability distribution which best represents the current state of knowledge about a system is the one with largest entropy,...

{1}{N}}\sum _{i=1}^{N}\log _{b}q(x_{i})} may also be interpreted as a cross-entropy: H ( p ~ , q ) = − ∑ x p ~ ( x ) log b ⁡ q ( x ) {\displaystyle H({\tilde...

Cross-entropy benchmarking (also referred to as XEB) is a quantum benchmarking protocol which can be used to demonstrate quantum supremacy. In XEB, a...

In physics, the Tsallis entropy is a generalization of the standard Boltzmann–Gibbs entropy. It is proportional to the expectation of the q-logarithm...

the relationship between maximizing the likelihood and minimizing the cross-entropy, URL (version: 2019-11-06): https://stats.stackexchange.com/q/364237...