English

Intrinsic entropies of log-concave distributions

Information Theory 2017-02-07 v1 math.IT

Abstract

The entropy of a random variable is well-known to equal the exponential growth rate of the volumes of its typical sets. In this paper, we show that for any log-concave random variable XX, the sequence of the nθth\lfloor n\theta \rfloor^{\text{th}} intrinsic volumes of the typical sets of XX in dimensions n1n \geq 1 grows exponentially with a well-defined rate. We denote this rate by hX(θ)h_X(\theta), and call it the θth\theta^{\text{th}} intrinsic entropy of XX. We show that hX(θ)h_X(\theta) is a continuous function of θ\theta over the range [0,1][0,1], thereby providing a smooth interpolation between the values 0 and h(X)h(X) at the endpoints 0 and 1, respectively.

Keywords

Cite

@article{arxiv.1702.01203,
  title  = {Intrinsic entropies of log-concave distributions},
  author = {Varun Jog and Venkat Anantharam},
  journal= {arXiv preprint arXiv:1702.01203},
  year   = {2017}
}

Comments

33 pages. A shorter version of this paper appeared in the proceedings of ISIT 2015