Log-concave Measure
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In mathematics, a Borel measure ''μ'' on ''n''-
dimension In physics and mathematics, the dimension of a mathematical space (or object) is informally defined as the minimum number of coordinates needed to specify any point within it. Thus, a line has a dimension of one (1D) because only one coor ...
al
Euclidean space Euclidean space is the fundamental space of geometry, intended to represent physical space. Originally, that is, in Euclid's ''Elements'', it was the three-dimensional space of Euclidean geometry, but in modern mathematics there are Euclidean sp ...
\mathbb^ is called logarithmically concave (or log-concave for short) if, for any compact subsets ''A'' and ''B'' of \mathbb^ and 0 < ''λ'' < 1, one has : \mu(\lambda A + (1-\lambda) B) \geq \mu(A)^\lambda \mu(B)^, where ''λ'' ''A'' + (1 − ''λ'') ''B'' denotes the
Minkowski sum In geometry, the Minkowski sum (also known as dilation) of two sets of position vectors ''A'' and ''B'' in Euclidean space is formed by adding each vector in ''A'' to each vector in ''B'', i.e., the set : A + B = \. Analogously, the Minkowsk ...
of ''λ'' ''A'' and (1 − ''λ'') ''B''.


Examples

The Brunn–Minkowski inequality asserts that the
Lebesgue measure In measure theory, a branch of mathematics, the Lebesgue measure, named after French mathematician Henri Lebesgue, is the standard way of assigning a measure to subsets of ''n''-dimensional Euclidean space. For ''n'' = 1, 2, or 3, it coincides ...
is log-concave. The restriction of the Lebesgue measure to any
convex set In geometry, a subset of a Euclidean space, or more generally an affine space over the reals, is convex if, given any two points in the subset, the subset contains the whole line segment that joins them. Equivalently, a convex set or a convex ...
is also log-concave. By a theorem of Borell, a probability measure on R^d is log-concave if and only if it has a density with respect to the Lebesgue measure on some affine hyperplane, and this density is a
logarithmically concave function In convex analysis, a non-negative function is logarithmically concave (or log-concave for short) if its domain is a convex set, and if it satisfies the inequality : f(\theta x + (1 - \theta) y) \geq f(x)^ f(y)^ for all and . If is strict ...
. Thus, any
Gaussian measure In mathematics, Gaussian measure is a Borel measure on finite-dimensional Euclidean space R''n'', closely related to the normal distribution in statistics. There is also a generalization to infinite-dimensional spaces. Gaussian measures are named ...
is log-concave. The Prékopa–Leindler inequality shows that a
convolution In mathematics (in particular, functional analysis), convolution is a mathematical operation on two functions ( and ) that produces a third function (f*g) that expresses how the shape of one is modified by the other. The term ''convolution' ...
of log-concave measures is log-concave.


See also

*
Convex measure In measure and probability theory in mathematics, a convex measure is a probability measure that — loosely put — does not assign more mass to any intermediate set "between" two measurable sets ''A'' and ''B'' than it does to ''A'' or ...
, a generalisation of this concept *
Logarithmically concave function In convex analysis, a non-negative function is logarithmically concave (or log-concave for short) if its domain is a convex set, and if it satisfies the inequality : f(\theta x + (1 - \theta) y) \geq f(x)^ f(y)^ for all and . If is strict ...


References

{{Measure theory Measures (measure theory)