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- W2607913557 abstract "We derive a lower bound on the differential entropy of a log-concave random variable $X$ in terms of the $p$-th absolute moment of $X$. The new bound leads to a reverse entropy power inequality with an explicit constant, and to new bounds on the rate-distortion function and the channel capacity. Specifically, we study the rate-distortion function for log-concave sources and distortion measure $| x - hat x|^r$, and we establish that the difference between the rate distortion function and the Shannon lower bound is at most $log(sqrt{pi e}) approx 1.5$ bits, independently of $r$ and the target distortion $d$. For mean-square error distortion, the difference is at most $log (sqrt{frac{pi e}{2}}) approx 1$ bits, regardless of $d$. We also provide bounds on the capacity of memoryless additive noise channels when the noise is log-concave. We show that the difference between the capacity of such channels and the capacity of the Gaussian channel with the same noise power is at most $log (sqrt{frac{pi e}{2}}) approx 1$ bits. Our results generalize to the case of vector $X$ with possibly dependent coordinates, and to $gamma$-concave random variables. Our proof technique leverages tools from convex geometry." @default.
- W2607913557 created "2017-05-05" @default.
- W2607913557 creator A5001633086 @default.
- W2607913557 creator A5062208766 @default.
- W2607913557 date "2018-03-09" @default.
- W2607913557 modified "2023-09-26" @default.
- W2607913557 title "A Lower Bound on the Differential Entropy of Log-Concave Random Vectors with Applications" @default.
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- W2607913557 doi "https://doi.org/10.3390/e20030185" @default.
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