All Questions
25 questions
1
vote
1
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100
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Does convergence of Radon transforms of a sequence of probability distributions implies convergence of the distributions themselves?
Let $P_1,P_2,\ldots $ be a sequence of absolutely continuous probability measures on $\mathbb R^n$, and let
$f_j:\mathbb R^n\to\mathbb R$ be their PDFs. Assume that $\operatorname{E}P_j = 0$ and $\...
- 128k
2
votes
1
answer
164
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Maximal entropy distribution on three variables knowing its marginals on any two
Observation 0: Given a finite set $X$, the probability distribution on $X$ with highest entropy is the uniform one. This is well known.
Observation 1: Given two finite sets $X,Y$ and two probability ...
- 301
3
votes
1
answer
407
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Relative entropy equality for a sequence of Bernoulli random variables
We are given two joint probability distributions, $p$ and $q$, of $n$ Bernoulli random variables $X_1, X_2, \ldots, X_n$.
We denote by $p(x_k\mid x^{k-1})$ the probability $\mathbb{P}_p(X_k=x_k\mid ...
- 1,677
7
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1
answer
305
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Why does non-decreasing entropy imply actual convergence to that max entropy distribution?
Let $X_n$ be i.i.d with finite variance. Let $\bar X_n=\frac 1n \sum_{i=1}^nX_i$. It is a famous result that the continuous/differential entropy of the normalized average is non-decreasing. $$\mathrm ...
- 128k
3
votes
1
answer
614
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An inequality relating the Kullback-Leibler divergence of two discrete distributions with constant reference distribution
Suppose that $D_{KL}(p_1\parallel q)<1$ and $D_{KL}(p_2\parallel q)<1$. I'm trying to show that either $D_{KL}(p_1\parallel p_2)$ or $D_{KL}(p_2\parallel p_1)$ will have an upper bound close to ...
2
votes
0
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69
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A distribution $\pi \propto \exp(-f)$ satisfies log-Sobolev inequality, does $\exp(-af)$ also satisfy LSI?
Assume a distribution $\pi \propto e^{-f}$ satisfies log-Sobolev inequality (LSI)
$$\forall \rho \in P(\mathbb{R}^n), \quad KL(\rho\| \pi) \le \frac{1}{2\lambda} I(\rho \| \pi)$$
with LSI constant $\...
- 21
2
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0
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264
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Prove or disprove a mutual information inequality
I have $n$ IID Bernoulli random variables denoted by $X_1,X_2,\ldots X_n$ with parameter $p$.
I am interested in knowing if the following inequality involving mutual information holds :
$\boxed{\max_{...
- 841
2
votes
1
answer
292
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Mutual information between two discrete random variables
I have 2 IID random variables $X_1$ and $X_2$ with $Bern(p)$ distribution. I have another binary random variable $Y$ taking values in $\{0,1\}$.
I am interested in comparing the following 2 mutual ...
- 128k
10
votes
3
answers
803
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Discrete entropy of the integer part of a random variable
Let $X$ be a real valued random variable. Of course, the integer part $\lfloor X \rfloor$ of $X$ is a discrete random variable taking values in $\mathbb{Z}$. We can therefore define its discrete ...
- 128k
1
vote
0
answers
146
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Using maximum entropy principle for joint probability estimation
Let $X_1, \dots, X_n, Y$ be random variables, each taking values in $\{0,1\}$. Assume that we are interested in estimating, for each $v=(v_1,\dots,v_n)\in \{0,1\}^n$, the probability
$$
p(v) = P[Y=1|...
- 7,182
1
vote
1
answer
417
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Obtaining the error term of binomial distribution's entropy from the differential entropy of a Gaussian distribution
It is known that the first order error term in the Shannon entropy formula for a binomial distribution is $1/n$ (for example, see the Wikipedia page Binomial distribution), where in the limit $n \to \...
CommunityBot
- 1
2
votes
1
answer
476
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Is total variation distance of normalized sum of random variables to Gaussian monotonic decreasing?
Let $X_1, X_2, \ldots$ be independent and identically distributed random variables with mean $0$ and variance $1$ and let $S_n = (X_1 + \cdots + X_n)/\sqrt{n}$ to be their normalized sum. Define $D_n$ ...
- 148k
21
votes
1
answer
32k
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How to compute KL-divergence when PMF contains 0s?
From the Wikipedia page on Kullback-Leibler divergence, the way to compute this metric is to utilize the following formula:
The way I understand this is to compute the PMFs of two given sample sets ...
0
votes
1
answer
582
views
Integrability of $\int \log(f(x)) f(x) dx$ for a probability density function $f$
I am looking for weak conditions when a probability density function $f$ on $\mathbb{R}^d$ has a finite integral
$$
\int_{\mathbb{R}^d} \log(f(x)) f(x) dx.
$$
Any references would be appreciated.
- 128k
2
votes
1
answer
181
views
Conditional entropy - solve example
Given a random variable $X$ that is uniformly distributed on $[-b,b]$ and $Y=g(X)$ with
$$g(x) = \begin{cases} 0, ~~~ x\in [-c,c] \\ x, ~~~ \text{else}\end{cases}$$
Now I want to compute the ...
- 131
18
votes
3
answers
3k
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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 $P$ ...
2
votes
1
answer
3k
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Discrete Maximum Entropy Distribution with given mean
For a given mean $\mu$, what is the entropy maximizing probability distribution on the nonnegative integers?
Different sources indicated either the geometric or the Poisson distribution for this. As ...
5
votes
1
answer
778
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Calculate channel capacity of general channel under constraint
Given a conditional distribution $P_{Y\mid X}$ I'd like to find the prior distribution $P_X$ that maximizes the mutual information $I(X;Y)$ with $P_Y(y)=\int P_{Y\mid X}(y\mid x)P_X(x) \, \text{d}x$ (...
CommunityBot
- 1
4
votes
0
answers
91
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What is the entropy of binomial decay?
Let's play a game. I start with $N$ indistinguishable tokens, and I wait $T$ turns. Every turn, each token has probability $p$ of disappearing. I want an analytic formula for the entropy of this ...
- 141
1
vote
4
answers
3k
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Differential Entropy of Random Signal
Prove that the Normal (Gaussian) Distribution with a given Variance $ {\sigma}^{2} $ maximizes the Differential Entropy among all distributions with defined and finite 1st Moment and Variance which ...
- 115
8
votes
2
answers
633
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Maximal entropy distribution with given conditionals
It is well known that of all the joint distributions $p(x,y)$ with fixed marginals $p(x),p(y)$, the one with the highest entropy is:
$$
p(x,y)=p(x)p(y).
$$
Suppose instead that we have conditionals. ...
CommunityBot
- 1
2
votes
1
answer
306
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About Renyi entropy
If one is given a joint probability distribution over a finite set of discrete random variables then I guess there a notion of $\alpha-$Renyi entropy defined for it as $S_\alpha (X_1,..,X_n) = \frac{...
- 10.4k
7
votes
2
answers
409
views
Estimating entropy conditional to an event
Take for example the measure $\mu(n)=n^2$ on $\{1, \ldots, N\}$ and a random variable $X$ distributed according to the probability obtained by normalizing $\mu$.
Does there exists a constant $K>0$...
- 2,560
1
vote
1
answer
304
views
Entropy on a draw from a random distribution.
Suppose I am attempting to calculate the entropy of a continuous, normally distributed random variable $X$, from the distribution $\mathcal{N}(\mu, \sigma)$. This is easy to to do - I just calculate
$...
CommunityBot
- 1
0
votes
1
answer
377
views
Robust entropy-like measure for analyzing uncertainity
I'm looking for a measure to analysis the uncertainty observed in a set of variables (with multivariate Gaussian distribution). So, I've tried conventional Shanon entropy (differential entropy) which ...
CommunityBot
- 1