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  2. Minkowski–Bouligand dimension - Wikipedia

    en.wikipedia.org/wiki/Minkowski–Bouligand...

    An interesting property of the upper box dimension not shared with either the lower box dimension or the Hausdorff dimension is the connection to set addition. If A and B are two sets in a Euclidean space, then A + B is formed by taking all the pairs of points a , b where a is from A and b is from B and adding a + b .

  3. Minkowski content - Wikipedia

    en.wikipedia.org/wiki/Minkowski_content

    In particular, the m-dimensional Minkowski content in R n is not a measure unless m = 0, in which case it is the counting measure. Indeed, clearly the Minkowski content assigns the same value to the set A as well as its closure. If A is a closed m-rectifiable set in R n, given as the image of a bounded set from R m under a Lipschitz function ...

  4. Validity (statistics) - Wikipedia

    en.wikipedia.org/wiki/Validity_(statistics)

    The validity of a measurement tool (for example, a test in education) is the degree to which the tool measures what it claims to measure. [3] Validity is based on the strength of a collection of different types of evidence (e.g. face validity, construct validity, etc.) described in greater detail below.

  5. Minkowski inequality - Wikipedia

    en.wikipedia.org/wiki/Minkowski_inequality

    In mathematical analysis, the Minkowski inequality establishes that the L p spaces are normed vector spaces.Let be a measure space, let < and let and be elements of (). Then + is in (), and we have the triangle inequality

  6. Box-counting content - Wikipedia

    en.wikipedia.org/wiki/Box-counting_content

    Let = [,] denote the unit interval. Note that the box-counting dimension ⁡ and the Minkowski dimension ⁡ coincide with a common value of 1; i.e. ⁡ = ⁡ = Now observe that (,) = ⌊ / ⌋ +, where ⌊ ⌋ denotes the integer part of .

  7. Minkowski problem for polytopes - Wikipedia

    en.wikipedia.org/wiki/Minkowski_problem_for_poly...

    For any -dimensional polytope, one can specify its collection of facet directions and measures by a finite set of -dimensional nonzero vectors, one per facet, pointing perpendicularly outward from the facet, with length equal to the ()-dimensional measure of its facet. [3]

  8. Shapley–Folkman lemma - Wikipedia

    en.wikipedia.org/wiki/Shapley–Folkman_lemma

    A probability measure is a finite measure, and the Shapley–Folkman lemma has applications in non-probabilistic measure theory, such as the theories of volume and of vector measures. The Shapley–Folkman lemma enables a refinement of the Brunn–Minkowski inequality , which bounds the volume of sums in terms of the volumes of their summand ...

  9. Minkowski functional - Wikipedia

    en.wikipedia.org/wiki/Minkowski_functional

    If is a subset of a real or complex vector space, then the Minkowski functional or gauge of is defined to be the function: [,], valued in the extended real numbers, defined by ():= {: >}, where the infimum of the empty set is defined to be positive infinity (which is not a real number so that () would then not be real-valued).