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...

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

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

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

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

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

{\displaystyle (x,y)} . Expressed in terms of the entropy H ( ⋅ ) {\displaystyle H(\cdot )} and the conditional entropy H ( ⋅ | ⋅ ) {\displaystyle H(\cdot |\cdot...

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

{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...

( 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:...

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

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

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

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...

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 )...

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

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

In physics, the von Neumann entropy, named after John von Neumann, is a measure of the statistical uncertainty within a description of a quantum system...

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...

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

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

Neumann entropy, S(ρ) the joint quantum entropy and S(ρA|ρB) a quantum generalization of conditional entropy (not to be confused with conditional quantum...

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

script, and noting that the Indus script appears to have a similar conditional entropy to Old Tamil. These scholars have proposed readings of many signs;...

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

Information theory Entropy Differential entropy Conditional entropy Joint entropy Mutual information Directed information Conditional mutual information...

interpreted within the context of information theory. Bayes factor Conditional entropy Conditional probability Empirical likelihood Likelihood principle Likelihood-ratio...

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

that tf–idf employs." The conditional entropy of a "randomly chosen" document in the corpus D {\displaystyle D} , conditional to the fact it contains a...