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A Venn diagram, also called a set diagram or logic diagram, shows all possible logical relations between a finite collection of different sets. These diagrams depict elements as points in the plane, and sets as regions inside closed curves. A Venn diagram consists of multiple overlapping closed curves, usually circles, each representing a set.
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 center lens of the 2-circle figure is called a vesica piscis, from Euclid. Two circles are also called Villarceau circles as a plane intersection of a torus. The areas inside one circle and outside the other circle is called a lune. The 3-circle figure resembles a depiction of Borromean rings and is used in 3-set theory Venn diagrams.
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.
Parts of these same circles are used to form the triquetra, a figure of three overlapping semicircles (each two of which form a vesica piscis symbol) that again has a Reuleaux triangle at its center; [76] just as the three circles of the Venn diagram may be interlaced to form the Borromean rings, the three circular arcs of the triquetra may be ...
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More than 100 pages use this file. The following list shows the first 100 pages that use this file only. A full list is available.. Abraham Fraenkel; Algebra of sets; Almost
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.