In mathematics, a Borel measure μ on n-dimensional Euclidean space ${\displaystyle \mathbb {R} ^{n}}$ is called logarithmically concave (or log-concave for short) if, for any compact subsets A and B of ${\displaystyle \mathbb {R} ^{n}}$ and 0 < λ < 1, one has

${\displaystyle \mu (\lambda A+(1-\lambda )B)\geq \mu (A)^{\lambda }\mu (B)^{1-\lambda },}$

where λ A + (1 − λ) B denotes the Minkowski sum of λ A and (1 − λ) B.^[1]

The Brunn–Minkowski inequality asserts that the Lebesgue measure is log-concave. The restriction of the Lebesgue measure to any convex set is also log-concave.

By a theorem of Borell,^[2] 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. Thus, any Gaussian measure is log-concave.

The Prékopa–Leindler inequality shows that a convolution of log-concave measures is log-concave.

• Convex measure, a generalisation of this concept
• Logarithmically concave function