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Log-concave distributions are necessary for a number of algorithms, e.g. adaptive rejection sampling. Every distribution with log-concave density is a maximum entropy probability distribution with specified mean μ and Deviation risk measure D . [ 2 ]
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.
The rows of Pascal's triangle are examples for logarithmically concave sequences. In mathematics, a sequence a = (a 0, a 1, ..., a n) of nonnegative real numbers is called a logarithmically concave sequence, or a log-concave sequence for short, if a i 2 ≥ a i−1 a i+1 holds for 0 < i < n.
An extension of rejection sampling that can be used to overcome this difficulty and efficiently sample from a wide variety of distributions (provided that they have log-concave density functions, which is in fact the case for most of the common distributions—even those whose density functions are not concave themselves) is known as adaptive ...
Log probabilities make some mathematical manipulations easier to perform. Optimization. Since most common probability distributions —notably the exponential family —are only logarithmically concave , [ 2 ] [ 3 ] and concavity of the objective function plays a key role in the maximization of a function such as probability, optimizers work ...
Every distribution with log-concave density is a maximal entropy distribution with specified mean μ and deviation risk measure D . [10] In particular, the maximal entropy distribution with specified mean and deviation is:
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Log-concave may refer to: Logarithmically concave function; Logarithmically concave measure; Logarithmically concave sequence This page was last edited on 22 ...