Search results
Results from the WOW.Com Content Network
Hypertropia is a condition of misalignment of the eyes ( strabismus ), whereby the visual axis of one eye is higher than the fellow fixating eye. Hypotropia is the similar condition, focus being on the eye with the visual axis lower than the fellow fixating eye. Dissociated vertical deviation is a special type of hypertropia leading to slow ...
Information theory is the mathematical study of the quantification, storage, and communication of information. The field was established and put on a firm footing by Claude Shannon in the 1940s, [ 1 ] though early contributions were made in the 1920s through the works of Harry Nyquist and Ralph Hartley.
In information theory, the entropy of a random variable is the average level of "information", "surprise", or "uncertainty" inherent to the variable's possible outcomes. Given a discrete random variable , which takes values in the set and is distributed according to , the entropy is where denotes the sum over the variable's possible values.
Information processing theory is the approach to the study of cognitive development evolved out of the American experimental tradition in psychology. Developmental psychologists who adopt the information processing perspective account for mental development in terms of maturational changes in basic components of a child's mind .
The defining expression for entropy in the theory of information established by Claude E. Shannon in 1948 is of the form: where is the probability of the message taken from the message space M, and b is the base of the logarithm used. Common values of b are 2, Euler's number e, and 10, and the unit of entropy is shannon (or bit) for b = 2, nat ...
Many of the concepts in information theory have separate definitions and formulas for continuous and discrete cases. For example, entropy is usually defined for discrete random variables, whereas for continuous random variables the related concept of differential entropy, written (), is used (see Cover and Thomas, 2006, chapter 8).
Graph entropy. In information theory, the graph entropy is a measure of the information rate achievable by communicating symbols over a channel in which certain pairs of values may be confused. [ 1] This measure, first introduced by Körner in the 1970s, [ 2][ 3] has since also proven itself useful in other settings, including combinatorics.
Quantum information is the information of the state of a quantum system. It is the basic entity of study in quantum information theory, [ 1][ 2][ 3] and can be manipulated using quantum information processing techniques. Quantum information refers to both the technical definition in terms of Von Neumann entropy and the general computational term.