In information theory, the conditional entropy quantifies the amount of information needed to describe the outcome of a random variable Y {\displaystyle...

The conditional quantum entropy is an entropy measure used in quantum information theory. It is a generalization of the conditional entropy of classical...

Despite similar notation, joint entropy should not be confused with cross-entropy. The conditional entropy or conditional uncertainty of X given random...

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

to a stochastic process. For a strongly stationary process, the conditional entropy for latest random variable eventually tend towards this rate value...

for example, differential entropy may be negative. The differential analogies of entropy, joint entropy, conditional entropy, and mutual information are...

Shannon entropy and its quantum generalization, the von Neumann entropy, one can define a conditional version of min-entropy. The conditional quantum...

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

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

y,z)dxdydz.} Alternatively, we may write in terms of joint and conditional entropies as I ( X ; Y | Z ) = H ( X , Z ) + H ( Y , Z ) − H ( X , Y , Z )...

{H} (X_{1})+\ldots +\mathrm {H} (X_{n})} Joint entropy is used in the definition of conditional entropy: 22  H ( X | Y ) = H ( X , Y ) − H ( Y ) {\displaystyle...

the joint entropy. This is equivalent to the fact that the conditional quantum entropy may be negative, while the classical conditional entropy may never...

joint, conditional differential entropy, and relative entropy are defined in a similar fashion. Unlike the discrete analog, the differential entropy has...

information theoretic Shannon entropy. The von Neumann entropy is also used in different forms (conditional entropies, relative entropies, etc.) in the framework...

marginal entropies, H ( X ∣ Y ) {\displaystyle \mathrm {H} (X\mid Y)} and H ( Y ∣ X ) {\displaystyle \mathrm {H} (Y\mid X)} are the conditional entropies, and...

( Y ∣ X ) {\displaystyle H(Y\mid X)} are the entropy of the output signal Y and the conditional entropy of the output signal given the input signal, respectively:...

X)\end{aligned}}} where H ( Y ∣ X ) {\displaystyle H(Y\mid X)} is the conditional entropy and D KL {\displaystyle D_{\text{KL}}} is the Kullback–Leibler divergence...

of Secrecy Systems conditional entropy conditional quantum entropy confusion and diffusion cross-entropy data compression entropic uncertainty (Hirchman...

In statistics, a maximum-entropy Markov model (MEMM), or conditional Markov model (CMM), is a graphical model for sequence labeling that combines features...

quantum relative entropy is a measure of distinguishability between two quantum states. It is the quantum mechanical analog of relative entropy. For simplicity...

of random variables and a measure over sets. Namely the joint entropy, conditional entropy, and mutual information can be considered as the measure of a...

conditional entropy of T {\displaystyle T} given the value of attribute a {\displaystyle a} . This is intuitively plausible when interpreting entropy...

variable e in terms of its entropy. One can then subtract the content of e that is irrelevant to h (given by its conditional entropy conditioned on h) from...

Rényi entropy is a quantity that generalizes various notions of entropy, including Hartley entropy, Shannon entropy, collision entropy, and min-entropy. The...

H\left(X|Y\right)=-\sum _{i,j}P(x_{i},y_{j})\log P\left(x_{i}|y_{j}\right)} is the conditional entropy, P ( e ) = P ( X ≠ X ~ ) {\displaystyle P(e)=P(X\neq {\tilde {X}})}...

relationships among Shannon's basic measures of information: entropy, joint entropy, conditional entropy and mutual information. Information diagrams are a useful...

identically-distributed random variable, and the operational meaning of the Shannon entropy. Named after Claude Shannon, the source coding theorem shows that, in the...

entropy of X. The above definition of transfer entropy has been extended by other types of entropy measures such as Rényi entropy. Transfer entropy is...

that quantum conditional entropies can be negative, and quantum mutual informations can exceed the classical bound of the marginal entropy. The strong...

{\displaystyle H} is simply the entropy of a symbol) and the continuous-valued case (where H {\displaystyle H} is the differential entropy instead). The definition...