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

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

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

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

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

In information theory and statistics, negentropy is used as a measure of distance to normality. The concept and phrase "negative entropy" was introduced...

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

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

December 2018). "Measuring Integrated Information: Comparison of Candidate Measures in Theory and Simulation". Entropy. 21 (1): 17. arXiv:1806.09373. Bibcode:2018Entrp...

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

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

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

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

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...

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

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...

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

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

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

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

In information theory, the entropy power inequality (EPI) is a result that relates to so-called "entropy power" of random variables. It shows that the...

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

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

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

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

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