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Upper bound on expectation of a convolution

Given probability densities $f, g\in L^p(\mathbb{R}^3), \ \forall p\geq 1$, with the same first and second moments \begin{align} & \int_{\mathbb{R}^3} v f(v)\,dv = \int_{\mathbb{R}^3} v g(v)\,dv, \...
Vasily Ilin's user avatar
3 votes
0 answers
80 views

Seeking strong bounds on KL-divergence and martingales for a hypothesis-testing inequality

Let's say we have a finite set $\mathcal{O}$ of observations, and let $\mathcal{C}(\Delta\mathcal{O})$ denote the space of closed convex sets of probability distributions. We have two hypotheses which ...
Alex Appel's user avatar
2 votes
1 answer
138 views

How to lower bound the absolute value of the difference of two Kullback-Leibler divergences given the constrains below?

Given that $\min (D[p_1||p_3],D[p_2||p_4])=a$, how to find a lower bound of the difference $\left \vert D[p_1\parallel p_2]-D[p_3\parallel p_4] \right\vert$ with respect to $a$? If the condition is ...
Richard Ben's user avatar
2 votes
0 answers
120 views

Information inequality for Renyi divergences

Let $X^1 \ldots X^n$ be random variables on $\mathbb{R}^d$ with an arbitrary joint probability distribution $\mu$ on $\mathbb{R}^{n \times d}$. Let $\nu = \nu^1 \times \ldots \times \nu^n$ be a ...
MatrixGeek1234's user avatar
1 vote
1 answer
182 views

Inequalities involving entropy: quantum discord and mutual information

My question is inspired by the following paper of Olivier and Żurek but for this question to be self-contained I will recall all the necessary definitions: for a quantum state $\rho$ we define the ...
truebaran's user avatar
  • 9,330
3 votes
1 answer
205 views

Bound on an integral representing a difference of two relative entropies

Let $ f : [0,1] \to \mathbb{R} $ be a function satisfying: 1.) $ |f(x)| \leqslant a $ for some $ a < 1 $, and 2.) $ \int_0^1 f(x) {\mathrm d}x = 0 $. I would like to know whether the following ...
aleph's user avatar
  • 503
2 votes
0 answers
118 views

Inequality for log-likelihood ratio

Let $ p, q $ be two probability densities on $ [0,1] $, strictly positive over $ (0,1) $. Let $ P $ be the cumulative function of $ p $, i.e., $ P(x) = \int_0^x p(x') \, \mathrm{d}x' $, $ x \in [0,1] $...
aleph's user avatar
  • 503
8 votes
1 answer
313 views

Lower bound $\int_0^1 \frac{|f'(x)|^2}{f} \,\mathrm{d} x$ by $\int_0^1 |f-1|^2\, \mathrm{d} x$

Assume that $f$ is a probability density on $x \in (0,1)$, I want to obtain a bound of the following form (if it is possible at all): $$ \int_0^1 \frac{|f'|^2}{f} \,\mathrm{d} x \geq C\,\int_0^1 |f-1|^...
Fei Cao's user avatar
  • 730
1 vote
1 answer
92 views

Lower bound $L_{1}$-metric with $L_{2}$-metric for bounded pdfs, on common support

Setup To clarify, let constants $0 < a < b < \infty$, and $p \in \mathbb{N}$ be fixed. Further let $B \subset \mathbb{R}^{p}$ be a fixed compact support. We then define the space of bounded (...
user4687531's user avatar
15 votes
1 answer
703 views

Information inequalities

What are the feasible $2^n$-tuples of entropies $h(S) := H(X_{i_1},\dots,X_{i_{|S|}})$ where $X_1,\dots,X_n$ are discrete random variables with some (unknown) joint probability distribution as $S=\{...
James Propp's user avatar
  • 19.7k
1 vote
1 answer
296 views

Lower bound for KL divergence of bounded densities and $L_{2}$ metric

I am currently reading "Smoothing of Multivariate Data" by Klemela. It contains Lemma 11.6, which upper and lower bounds the KL-divergence of two densities in terms of the $L_{2}$-metric. ...
user4687531's user avatar
1 vote
1 answer
224 views

A question about mutual information

Let $A$ and $B$ be two, possibly dependent, random variables, and let $X$ be a random variable independent of $(A,B)$. For simplicity, let's concern ourselves with discrete random variables. Is the ...
DeepC's user avatar
  • 63
17 votes
4 answers
2k views

Geometric interpretations of the exponential of entropy

Question: Might there be a natural geometric interpretation of the exponential of entropy in Classical and Quantum Information theory? This question occurred to me recently via a geometric inequality ...
Aidan Rocke's user avatar
  • 3,871
5 votes
4 answers
4k views

Is there an inequality relation between KL-divergence and $L_2$ norm?

According to the Pinsker inequality, we have the following inequality: \begin{equation} \delta_{TV} (p, q)^2 \leq \frac{1}{2} D_{KL}(p,q), \end{equation} where $\delta_{TV} (\cdot, \cdot)$ and $D_{KL}...
Ze-Nan Li's user avatar
  • 175
5 votes
1 answer
150 views

Kullback–Leibler chains

The following question was asked and then deleted by the post author: Let $P$ and $Q$ be two probability distributions defined over the same space, with $KL(P \parallel Q) < \infty$. For $\epsilon ...
Iosif Pinelis's user avatar
0 votes
1 answer
181 views

Bounding information of expression

Cross posted to theory exchange - https://cstheory.stackexchange.com/questions/45610/bounding-information-of-expression Suppose $u_1,\ldots,u_n$ are uniformly iid in $\{0,1\}$. Let $x_1,\ldots,x_n$ ...
Andy's user avatar
  • 515
4 votes
1 answer
358 views

Bound for type of correlation measure

Assume you have a finite, discrete probability distribution for a joint random variable and such that $P(X=i,Y=j) = p_{i,j}$ for $i \in \{1, \dots, |X|\},j \in \{1, \dots, |Y|\}$. The marginal ...
Paul's user avatar
  • 51
18 votes
3 answers
3k views

Entropy and total variation distance

Let $X$, $Y$ be discrete random variables taking values within the same set of $N$ elements. Let the total variation distance $|P-Q|$ (which is half the $L_1$ distance between the distributions of $P$ ...
H A Helfgott's user avatar
  • 20.2k
0 votes
0 answers
250 views

Can we make two random variables independent at a low cost?

Let $X$ and $Y$ be two discrete random variables with joint probability mass function $p(x,y)$ such that $$\|p(x,y)-p(x)p(y)\|_1=\sum_{x\in\mathcal{X},y\in\mathcal{Y}}|p(x,y)-p(x)p(y)|\leq\epsilon$$ ...
Math_Y's user avatar
  • 287
8 votes
2 answers
4k views

Lower bounds on Kullback-Leibler divergence

This was originally a question on Cross Validated. Are there any (nontrivial) lower bounds on the Kullback-Leibler divergence $KL(f\Vert g)$ between two measures / densities? Informally, I am ...
JohnA's user avatar
  • 710
3 votes
1 answer
226 views

Finding a short proof for a certain information theoretic inequality

The following information theoretic inequality is needed in my work. Let $n, m, n_1, n_2, \dots, n_k \in \mathbb{Z}^+$ such that $m < n = n_1 + n_2 + \dots + n_k$. I would like to prove that with ...
Lwins's user avatar
  • 1,551
1 vote
2 answers
275 views

A corollary of Gibbs' inequality

Gibbs' inequality is equivalent to: \begin{equation} \sum_{i} \ln q_i^{p_i}-\ln p_i^{p_i} \leq 0 \end{equation} where $p_i,q_i \in [0,1]$ and $\sum_i p_i = \sum_i q_i=1$. Now, a friend of mine ...
Aidan Rocke's user avatar
  • 3,871
3 votes
1 answer
330 views

Does this probability distance metric have an official name?

Let us define a distance metric between two joint probability math functions $p(x,y)$ and $q(x,y)$ as in the following \begin{align} \sum_{y}\sqrt{\sum_{x}p(x)\left(p(y|x)-q(y|x)\right)^2}. \end{...
Math_Y's user avatar
  • 287
3 votes
1 answer
370 views

Entropy of average of two distributions

Let $\mu,\nu$ be two distributions on the same discrete space. Is it true that $$\mathrm{H}\left(\frac{\mu+\nu}{2}\right) \ge \mathbb{E}_{xy}-\log\left(\frac{\sqrt{\mu(x)\nu(y)}}{2} + \frac{\langle\...
boinkboink's user avatar
2 votes
1 answer
280 views

How much can KL divergence decrease by diluting the reference distribution

Let $\Omega$ be a countable set and $\mu,\nu\colon\Omega\to[0,1]$ be distributions on $\Omega$, that is we have $\sum_{x\in\Omega}\mu(x)=1$ and likewise for $\nu$. The Kullback-Leibler divergence of $\...
boinkboink's user avatar
4 votes
1 answer
346 views

Sharpened Pinsker inequality for special case

Let $B(p)$ denote the Bernoulli distribution over $\{0,1\}$ and $B(p)^n$ the corresponding product distribution over $\{0,1\}^n$. For $n>1$ and $0<x<1$, define $$P_n(x):=B(\frac12+\frac x2)^n$...
Aryeh Kontorovich's user avatar
8 votes
2 answers
486 views

concentration inequality for entropy from sample

Consider a measure $\mu$ on a finite set, and let $x_1, \ldots, x_n$ be i.i.d samples from $\mu$. Then the expression $S_n = -\frac{1}{n} \sum_{i=1}^n \log \mu(x_i)$ converges by a.s. to the entropy $...
komark's user avatar
  • 83
3 votes
0 answers
234 views

Will relative entropy increase with majorization?

Suppose that three probability distributions have the relation $P\succ Q\succ R$, then do the following relations between their relative entropy valid? (assume that $p_1\geq p_2\geq\cdots \geq p_n$ ...
prime's user avatar
  • 31
3 votes
0 answers
494 views

Maximization of a total variation distance subject to another total variation distance in Markov chain

Suppose two dependent random variables $X$ and $V$ from finite alphabets $\mathcal{V}$ and $\mathcal{X}$ with known joint and marginal distributions are given. Let $P_{XV}$ and $P_X$ and $P_V$ are the ...
math-Student's user avatar
  • 1,109
6 votes
0 answers
342 views

Maximizing Renyi entropy for a certain channel

The channel under consideration is $T = A + B$, where $A$ and $B$ take on values in $\{0, 1\}$ according to a probability mass function. Let (joint) random vector $(A_1, A_2,\ldots, A_n)$ be denoted ...
gandalfthegreat's user avatar
37 votes
3 answers
3k views

An entropy inequality

Let $X,Y$ be probability measures on $\{1,2,\dots,n\}$, and set $K=\sum_i\sqrt{X(i)Y(i)}$ so that $Z:=\frac{1}{K}\sqrt{XY}$ is also a probability measure on $\{1,2,\dots,n\}$. How can we prove the ...
Eric Naslund's user avatar
  • 11.4k
16 votes
7 answers
6k views

Understanding Gibbs's inequality

Short version Gibbs's inequality is a simple inequality for real numbers, usually understood information-theoretically. In the jargon, it states that for two probability measures on a finite set, ...
Tom Leinster's user avatar
  • 27.7k
5 votes
1 answer
790 views

Upper bound on joint Renyi entropy

Renyi entropy of a random pair $(X,Y)$ with probability distribution $p_{X,Y}$ is defined by \begin{equation} H_\alpha(X,Y) = \frac{1}{1-\alpha}\log\sum_{x,y} p_{X,Y}(x,y)^\alpha. \end{equation} ...
newuser's user avatar
  • 61
12 votes
2 answers
2k views

Proving a messy inequality

EDIT: After much work I was able to reduce the inequality to a single variable function which I need to show is non-positive. That function is (for $0\leq p\leq\frac{1}{2}$) $$\frac{p^2(\log(p))^2 - (...
VSJ's user avatar
  • 1,034
4 votes
1 answer
1k views

Generalizing inequality relating Euclidean distance & Frobenius norm to Bregman divergences such as relative entropy & von Neumann divergence

Motivation- A Special Case Supposing $A,B\in\mathbb{S}^{m\times m}$ are symmetric positive semi-definite (SPD) matrices and $\mathbf{x}\in\mathbb{R}^m$ is a unit vector where $\|\mathbf{x}\|=1$, we ...
ppyang's user avatar
  • 607
9 votes
2 answers
462 views

Entropy conjecture for distributions over $\mathbb{Z}_n$

Suppose we have two independent random variables $X$ (with distribution $p_X$) and $Y$ (with distribution $p_Y$) which take values in the cyclic group $\mathbb{Z}_n$. Let $Z = X +Y$, where the ...
VSJ's user avatar
  • 1,034
5 votes
2 answers
1k views

Inequality involving probability measures [closed]

I have been working on a problem(alternate minimization) where I want to establish an inequality in which I am stuck. An $\alpha$- parameterized version of the divergence(Kullback-Leibler) takes the ...
Ashok's user avatar
  • 779
2 votes
3 answers
593 views

l^p space inequality related to compressed sensing

I'm trying to read Donoho's 2004 paper Compressed Sensing and am having trouble with a supposedly trivial statement (equation 1.2 on page 3). He makes the sparsity assumption on $\theta \in \mathbb{R}...
Martin Leslie's user avatar