We explore two generalizations of the Cauchy-Schwarz Bessel’s inequality and the Selberg inequality and their application to probability theory. We then give a tautological proof of the De Caen-Selberg Inequality and a proof of the second Borel-Cantelli Lemma with negative dependence. We finish with a suggestion of how linear operator theory can help us understand the tightness of many Selberg-...

Hashing of databases is considered from the point of view of information and coding theory. The records of a database are represented as binary vectors of the same length stored in the external memory of a computer. The task is formulated as follows: given a pattern and a fixed size of working memory, form the set of addresses of records that can disagree with the pattern in the number of posit...

We address the recovery of sparse vectors in an overcomplete, linear and noisy multiple measurement framework, where the measurement matrix is known upto a permutation of its rows. We derive sparse Bayesian learning (SBL) based updates for joint recovery of the unknown sparse vectors and the sensing order, represented using a permutation matrix. We model the sparse vectors using multiple uncorr...

Abstract In this article, we take a probabilistic look at Hölder's inequality, considering the ratio of terms in classical Hölder inequality for random vectors . We prove central limit theorem ratio, which then allows us to reverse up multiplicative constant with high probability. The models randomness include uniform distribution on balls and spheres. also provide Berry–Esseen–type result larg...

Let Fn = (F1,n, ...., Fd,n), n > 1, be a sequence of random vectors such that, for every j = 1, ..., d, the random variable Fj,n belongs to a fixed Wiener chaos of a Gaussian field. We show that, as n → ∞, the components of Fn are asymptotically independent if and only if Cov(F 2 i,n, F 2 j,n) → 0 for every i 6= j. Our findings are based on a novel inequality for vectors of multiple Wiener-Itô ...

We study the relation between stochastic domination of an innnitely divisible random vector X by another innnitely divisible random vector Y and their corresponding L evy measures. The results are used to derive a Slepian-type inequality for a general class of symmetric innnitely divisible random vectors.

We prove that the exponent of the entropy of one dimensional projections of a log-concave random vector defines a 1/5-seminorm. We make two conjectures concerning reverse entropy power inequalities in the log-concave setting and discuss some examples. 2010 Mathematics Subject Classification. Primary 94A17; Secondary 52A40, 60E15.

We investigate the problem of successive refinement for Wyner-Ziv coding with degraded side information and obtain a complete characterization rate region quadratic vector Gaussian case. The achievability part is based on evaluation Tian-Diggavi inner bound that involves auxiliary random vectors. For converse part, matching outer obtained aid new extremal inequality. Herein, proof this inequali...