Network theoryIn mathematics, computer science and network science, network theory is a part of graph theory. It defines networks as graphs where the nodes or edges possess attributes. Network theory analyses these networks over the symmetric relations or asymmetric relations between their (discrete) components. Network theory has applications in many disciplines, including statistical physics, particle physics, computer science, electrical engineering, biology, archaeology, linguistics, economics, finance, operations research, climatology, ecology, public health, sociology, psychology, and neuroscience.
Kullback–Leibler divergenceIn mathematical statistics, the Kullback–Leibler divergence (also called relative entropy and I-divergence), denoted , is a type of statistical distance: a measure of how one probability distribution P is different from a second, reference probability distribution Q. A simple interpretation of the KL divergence of P from Q is the expected excess surprise from using Q as a model when the actual distribution is P.
Facilitated communicationFacilitated communication (FC), or supported typing, is a scientifically discredited technique that attempts to aid communication by people with autism or other communication disabilities who are non-verbal. The facilitator guides the disabled person's arm or hand and attempts to help them type on a keyboard or other device. There is widespread agreement within the scientific community and among disability advocacy organizations that FC is a pseudoscience.
Shared mediumIn telecommunication, a shared medium is a medium or channel of information transfer that serves more than one user at the same time. In order for most channels to function correctly, no more than one user can be transmitting at a time, so a channel access method must always be in effect. In circuit switching, each user typically gets a fixed share of the channel capacity. A multiplexing scheme divides up the capacity of the medium. Common multiplexing schemes include time-division multiplexing and frequency-division multiplexing.
State space (computer science)In computer science, a state space is a discrete space representing the set of all possible configurations of a "system". It is a useful abstraction for reasoning about the behavior of a given system and is widely used in the fields of artificial intelligence and game theory. For instance, the toy problem Vacuum World has a discrete finite state space in which there are a limited set of configurations that the vacuum and dirt can be in. A "counter" system, where states are the natural numbers starting at 1 and are incremented over time has an infinite discrete state space.
Maximum satisfiability problemIn computational complexity theory, the maximum satisfiability problem (MAX-SAT) is the problem of determining the maximum number of clauses, of a given Boolean formula in conjunctive normal form, that can be made true by an assignment of truth values to the variables of the formula. It is a generalization of the Boolean satisfiability problem, which asks whether there exists a truth assignment that makes all clauses true. The conjunctive normal form formula is not satisfiable: no matter which truth values are assigned to its two variables, at least one of its four clauses will be false.
State space (physics)In physics, a state space is an abstract space in which different "positions" represent, not literal locations, but rather states of some physical system. This makes it a type of phase space. Specifically, in quantum mechanics a state space is a complex Hilbert space in which each unit vector represents a different state that could come out of a measurement. Each unit vector specifies a different dimension, so the numbers of dimensions in this Hilbert space depends on the system we choose to describe.
Standard probability spaceIn probability theory, a standard probability space, also called Lebesgue–Rokhlin probability space or just Lebesgue space (the latter term is ambiguous) is a probability space satisfying certain assumptions introduced by Vladimir Rokhlin in 1940. Informally, it is a probability space consisting of an interval and/or a finite or countable number of atoms. The theory of standard probability spaces was started by von Neumann in 1932 and shaped by Vladimir Rokhlin in 1940.
Classified information in the United StatesThe United States government classification system is established under Executive Order 13526, the latest in a long series of executive orders on the topic beginning in 1951. Issued by President Barack Obama in 2009, Executive Order 13526 replaced earlier executive orders on the topic and modified the regulations codified to 32 C.F.R. 2001. It lays out the system of classification, declassification, and handling of national security information generated by the U.S.
Approximations of πApproximations for the mathematical constant pi (pi) in the history of mathematics reached an accuracy within 0.04% of the true value before the beginning of the Common Era. In Chinese mathematics, this was improved to approximations correct to what corresponds to about seven decimal digits by the 5th century. Further progress was not made until the 15th century (through the efforts of Jamshīd al-Kāshī).
