conditional entropy

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• Conditional entropy — Individual (H(X),H(Y)), joint (H(X,Y)), and conditional entropies for a pair of correlated subsystems X,Y with mutual information I(X; Y). In information theory, the conditional entropy (or equivocation) quantifies the remaining entropy (i.e.… …   Wikipedia

• Conditional quantum entropy — The conditional quantum entropy is an entropy measure used in quantum information theory. It is a generalization of the conditional entropy of classical information theory. The conditional entropy is written S(ρ | σ), or H(ρ | σ), depending on… …   Wikipedia

• Entropy (information theory) — In information theory, entropy is a measure of the uncertainty associated with a random variable. The term by itself in this context usually refers to the Shannon entropy, which quantifies, in the sense of an expected value, the information… …   Wikipedia

• Conditional mutual information — In probability theory, and in particular, information theory, the conditional mutual information is, in its most basic form, the expected value of the mutual information of two random variables given the value of a third. Contents 1 Definition 2… …   Wikipedia

• Entropy of mixing — The entropy of mixing is the change in the configuration entropy, an extensive thermodynamic quantity, when two different chemical substances or components are mixed. This entropy change must be positive since there is more uncertainty about the… …   Wikipedia

• Conditional random field — A conditional random field (CRF) is a statistical modelling method often applied in pattern recognition. More specifically it is a type of discriminative undirected probabilistic graphical model. It is used to encode known relationships between… …   Wikipedia

• Joint quantum entropy — The joint quantum entropy generalizes the classical joint entropy to the context of quantum information theory. Intuitively, given two quantum states ho and sigma, represented as density operators that are subparts of a quantum system, the joint… …   Wikipedia

• Joint entropy — The joint entropy is an entropy measure used in information theory. The joint entropy measures how much entropy is contained in a joint system of two random variables. If the random variables are X and Y, the joint entropy is written H(X,Y). Like …   Wikipedia

• Cross entropy — In information theory, the cross entropy between two probability distributions measures the average number of bits needed to identify an event from a set of possibilities, if a coding scheme is used based on a given probability distribution q,… …   Wikipedia

• Differential entropy — (also referred to as continuous entropy) is a concept in information theory that extends the idea of (Shannon) entropy, a measure of average surprisal of a random variable, to continuous probability distributions. Contents 1 Definition 2… …   Wikipedia

• Maximum-entropy Markov model — MEMM redirects here. For the German Nordic combined skier, see Silvio Memm. In machine learning, a maximum entropy Markov model (MEMM), or conditional Markov model (CMM), is a graphical model for sequence labeling that combines features of hidden …   Wikipedia