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Maciej Bialobrzeski

dblp:300/4133 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2023
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Theory of computation · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Information theory · 100%

Topics — the 1 heaviest of 1, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information theory › information measures › entropy › generalized entropy
rényi entropy
0.712023
Rényi Entropy and Variance Comparison for Symmetric Log-Concave Random Variables · IEEE Trans. Inf. Theory 2023
YearPublicationVenuePosition
2023 Rényi Entropy and Variance Comparison for Symmetric Log-Concave Random Variables
abstract
We show that for any$\alpha >0$the Rényi entropy of order$\alpha $is minimized, among all symmetric log-concave random variables with fixed variance, either for a uniform distribution or for a two sided exponential distribution. The first case occurs for$\alpha \in (0,\alpha ^{\ast}]$and the second case for$\alpha \in [\alpha ^{\ast},\infty$), where$\alpha ^{\ast}$satisfies the equation$2 \log \alpha ^{\ast}= (\alpha ^{\ast}-1) \log 6$, that is$\alpha ^{\ast} \approx 1.241$. We deduce that the one-sided exponential distribution minimizes Rényi entropy of order$\alpha \geq 2$among all log-concave random variables with fixed variance.
Maciej Bialobrzeski, Piotr Nayar
IEEE Trans. Inf. Theory1