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

k_{B}} is the Boltzmann constant. The defining expression for entropy in the theory of information established by Claude E. Shannon in 1948 is of the form:...

in information theory are mutual information, channel capacity, error exponents, and relative entropy. Important sub-fields of information theory include...

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

arguing that the entropy of statistical mechanics and the information entropy of information theory are the same concept. Consequently, statistical mechanics...

In information theory, joint entropy is a measure of the uncertainty associated with a set of variables. The joint Shannon entropy (in bits) of two discrete...

In information theory, the Rényi entropy is a quantity that generalizes various notions of entropy, including Hartley entropy, Shannon entropy, collision...

In statistics and information theory, a maximum entropy probability distribution has entropy that is at least as great as that of all other members of...

In information theory and statistics, negentropy is used as a measure of distance to normality. It is also known as negative entropy or syntropy. The...

In information theory, an entropy coding (or entropy encoding) is any lossless data compression method that attempts to approach the lower bound declared...

In the mathematical theory of probability, the entropy rate or source information rate is a function assigning an entropy to a stochastic process. For...

concept of mutual information is intimately linked to that of entropy of a random variable, a fundamental notion in information theory that quantifies the...

Differential entropy (also referred to as continuous entropy) is a concept in information theory that began as an attempt by Claude Shannon to extend...

coined the term entropy. Since the mid-20th century the concept of entropy has found application in the field of information theory, describing an analogous...

message source Differential entropy, a generalization of Entropy (information theory) to continuous random variables Entropy of entanglement, related to...

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

uncertainty) entropy encoding entropy (information theory) Fisher information Hick's law Huffman coding information bottleneck method information theoretic...

Entropic gravity, also known as emergent gravity, is a theory in modern physics that describes gravity as an entropic force—a force with macro-scale homogeneity...

using quantum information processing techniques. Quantum information refers to both the technical definition in terms of Von Neumann entropy and the general...

inference techniques rooted in Shannon information theory, Bayesian probability, and the principle of maximum entropy. These techniques are relevant to any...

In information theory, the binary entropy function, denoted H ⁡ ( p ) {\displaystyle \operatorname {H} (p)} or H b ⁡ ( p ) {\displaystyle \operatorname...

distance Information gain in decision trees Information gain ratio Information theory and measure theory Jensen–Shannon divergence Quantum relative entropy Solomon...

In statistical mechanics, entropy is formulated as a statistical property using probability theory. The statistical entropy perspective was introduced...

science, climate change and information systems including the transmission of information in telecommunication. Entropy is central to the second law...

used measure of information content, now known as Shannon entropy. As an objective measure of the quantity of information, Shannon entropy has been enormously...

The joint quantum entropy generalizes the classical joint entropy to the context of quantum information theory. Intuitively, given two quantum states ρ...

Rate–distortion theory is a major branch of information theory which provides the theoretical foundations for lossy data compression; it addresses the...

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

Quantum Fisher information Other measures employed in information theory: Entropy (information theory) Kullback–Leibler divergence Self-information Robert, Christian...

random variable. The Shannon information is closely related to entropy, which is the expected value of the self-information of a random variable, quantifying...