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A Venn diagram is a widely used diagram style that shows the logical relation between sets, popularized by John Venn (1834–1923) in the 1880s. The diagrams are used to teach elementary set theory, and to illustrate simple set relationships in probability, logic, statistics, linguistics and computer science.
The commonly-used diagram for the Borromean rings consists of three equal circles centered at the points of an equilateral triangle, close enough together that their interiors have a common intersection (such as in a Venn diagram or the three circles used to define the Reuleaux triangle).
The three Venn diagrams in the figure below represent respectively conjunction x ∧ y, disjunction x ∨ y, and complement ¬x. Figure 2. Venn diagrams for conjunction, disjunction, and complement. For conjunction, the region inside both circles is shaded to indicate that x ∧ y is 1 when both variables are 1.
The first diagram in this article shows three circles (one for each parity bit) and encloses data bits that each parity bit covers. The second diagram (shown to the right) is identical but, instead, the bit positions are marked. For the remainder of this section, the following 4 bits (shown as a column vector) will be used as a running example:
Graphical methods represent the required logical function by a diagram representing the logic variables and value of the function. By manipulating or inspecting a diagram, much tedious calculation may be eliminated. Graphical minimization methods for two-level logic include: Euler diagram (aka Eulerian circle) (1768) by Leonhard P. Euler (1707 ...
De Morgan's laws represented with Venn diagrams.In each case, the resultant set is the set of all points in any shade of blue. In propositional logic and Boolean algebra, De Morgan's laws, [1] [2] [3] also known as De Morgan's theorem, [4] are a pair of transformation rules that are both valid rules of inference.
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The circle on the right (blue and violet) is (), with the blue being (). The violet is the mutual information I ( X ; Y ) {\displaystyle \operatorname {I} (X;Y)} . In probability theory and information theory , the mutual information ( MI ) of two random variables is a measure of the mutual dependence between the two variables.