This tag is for [Entropy](http://en.wikipedia.org/wiki/Entropy_(information_theory)) in Mathematics.

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Maximum entropy joint distribution from marginals?

How does one find the maximum entropy joint distribution of two random variables X and Y given their marginal probability mass functions? I know: I have the marginals, meaning p(x) and p(y) are ...
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Trying to understand the math in a neuroscience article by Karl Friston

I am trying to understand a neuroscience article by Karl Friston. In it he gives three equations that are, as I understand him, equivalent or inter-convertertable and refer to both physical and ...
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Finding a specific sequence of digits in pi

Looking at the pifs project on GitHub and this question on SO has made me curious as to how feasible it is to find a specific sequence of digits within Pi. Essentially, on average, how many digits of ...
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Estimating the entropy

Given a discrete random variable $X$, I would like to estimate the entropy of $Y=f(X)$ by sampling. I can sample uniformly from $X$. The samples are just random vectors of length $n$ where the entries ...
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Notation of cross entropy

I have a question regarding a notation that seems to be very usual. For starters, cross entropy is defined by: \begin{align}H(X, q) &= H(X) + D(p||q) \\ & =-\sum_x p(x)\log_2 q(x)\end{align} ...
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A general definition of Entropy (i.e. may or may not be expectation of the Log of the probabilities) [closed]

Entropy may be defined as Entropy = Σ G(p(x)) Where 'G' is any function that goes asymptotically to plus infinity as it approaches zero from the positive side and is monotonic between 0 and 1 ...
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Definition of entropy of an ergodic measure

I'm reading a paper in which it is stated that The entropy of an ergodic measure is defined as $$\lim_{n \to \infty} -\frac{1}{n} \sum_{|w|=n} \mu[w] \log \mu[w].\tag{1} \label{eq:1}$$ Here ...
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when it is conditional entropy minimized?

for example let us consider following table know that entropy of variable is maximum when it is equally distributed,all of it's variable has equal probability,but what about joint entropy or ...
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25 views

calculate channel capacity and maximum conditional entropy

i want to know when it is equal channel capacity or $I(X,Y)$ maximum or where $I(X,Y)=H(X)-H(X\mid Y)=H(Y)-H(Y\mid X)$ now if we have two random variable with some specific distribution ...
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conditional entropy calculation

let us consider following table i am asked to calculate conditional entropy,from table i have understood everything,for instance how to calculate marginal probabilities,also i know formula for ...
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207 views

Is maximizing the Shannon differential entropy equivalent to minimizing the predictability and/or minimizing the maximum density?

For a real-valued, 1-dimensional, continuous random variable X with density f(x), I am trying to determine if maximizing the Shannon differential entropy of f(x) is mathematically equivalent to ...
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continuous, non-additive set function

I am looking for a non-additive, continuous set function from a simplex $\Pi_{n}$ of finite dimension $n-1$ into $[0,\infty]$. The motivation is as follows. Shannon defined the entropy ...
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What's the sum of all events? (not the sum of all probabilites of events)

I need to calculate the entropy $h(X|Y)$, where $Y=X^2$. In this case, I suppose $\mathrm p(x|y)=\frac{1}{2}$. For the entropy \begin{align} h(X|Y) &= \int\limits_y \mathrm p(y)\ h(X|Y=y)\ ...
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Compressing binary numbers

If I have a arbitrarily long random binary number with the condition that the probability that a given digit is 0 and 1 is 1/4 and 3/4, respectively. What is the best way to compress this into a ...
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How is the formula of Shannon Entropy derived?

From this slide, it's said that the smallest possible number of bits per symbol is as the Shannon Entropy formula defined: I've read this post, and still not quite understand how is this formula ...
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Entropy of the induced transformation

I need help with this problem: Let $(X,\mathcal{B},\mu,T)$ be a ergodic dynamical system in the probability space $(X,\mathcal{B},\mu)$. Let $A \in \mathcal{B}$ with $\mu(A)>0$. We define the ...
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Few questions regarding applications of conditional entopy

I have the idea of entropy and conditional probability etc and having few conditional entropy related questions: 1. What does actually conditional entropy $H(Y|X)$ mean? How can we explain this term ...
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Mutual information decrease with coarse-graining

Let $X,A,Y,B,C,D$ be random binary variables. $D$ is independent from $X,A,C$ and $C$ is independent from $Y,B,D$. Is it true that: If $I(Y:B|D=0)\leq \epsilon$ then $I(X\oplus Y:A\oplus ...
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Gaussian distribution variance estimation

It's well known if I have a process generating normally distribuited data, I can estimate the parameters of the gaussian function: ...
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Generlized Entropy compared to Generalized Dimension

I am currently reading the following paper by F.Takens: Multifractal analysis of dimensions and entropies. This paper discusses two different measures. One is generalized entropies and the other is ...
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Prove that bitstrings with 1/0-ratio different from 50/50 are compressable

I'm looking for a proof, that $$ \sum_{i=0}^{\lambda n} \binom{n}{i} \le 2^{nH(\lambda)} $$ with $n>0$, $0 \le \lambda \le 1/2$ and $ H(\lambda)=-[\lambda log \lambda + (1-\lambda) log (1-\lambda)] ...
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Variational Methods, why KL divergence is the difference between true distribution and approximating distribution.

Likelihood = $L(\textbf{w}) = P(V\mid \textbf{w})$. $$\ln P(V\mid \textbf{w}) = \ln \sum_H P(H,V\mid \textbf{w})$$ $$= \ln \sum_H Q(H\mid V)\frac{P(H,V\mid \textbf{w})}{Q(H\mid V)}$$ $$\geq ...
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What's the Shannon entropy of the prime numbers?

Here's a note that calculates it as 1. Do you know of any other calculations? http://www.math-math.com/2014/05/shannon-entropy-shannon-entropy-of.html
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necessity of uniform continuity for topological entropy

I am referring to Walters' book "Introduction to Ergodic Theory." When he defines the concept of topological entropy he always assumes that the transformation $T: X \rightarrow X$ is uniformly ...
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probabilistically what can we say about the next throw of a coin after n throws

this may sound easy or hard or whatever but i cant seem to find anything after searching around for a similar question/answer The question is this: What can we say (probabilistically) about the next ...
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What connections between computer science and ergodic theory?

I have a background in ergodic theory, but I am switching to computer science/programming. I would like to know if tools from ergodic theory could be useful, especially something around coding of ...
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Entropy of sum is sum of entropies

Having $X$ and $Y$ discrete random variables above finite set. Z is defined as $Z=X+Y$ when does the following happen: $$H(Z)=H(X)+H(Y)$$
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Entropy of sum of two Uniform random variables

say $X$ and $Y$ are two identical, independent and discrete Uniform random variables and $Z=X+Y$. I do not know more about the random variables. Assuming $H(\cdot)$ to be the entropy of a random ...
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intuition for entropy solutions

For a hyperbolic PDE of the form $$u_t + f(u)_x = 0$$ it turns out that the right notion of solution is entropy solution. Now, the notion of classical solutions are obviously very natural, and also ...
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What is the “true” entropy of a binary string?

Consider an infinite binary string $\sigma$ and define its entropy $$H_1 = -(p_0 \log_2 p_0 + p_1 \log_2 p_1)$$ with $p_i = \lim_{N\rightarrow \infty} N(i)/N$, $N(i)$ the number of $i$'s among the ...
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Problem with calculating probability of symbols

I've a $100 \times 100$ binary matrix it`s constructed with this probability table : i want to apply extended Huffman on this matrix my idea is to compress each column individually . - so starting ...
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Continuity of Shannon Entropy with respect to KL-Divergence distance

I am trying to prove the following statement which seems to be trivial but I cannot: Suppose $p$ and $q$ are two distributions. If $D(p||q) < \epsilon$ and $D(q||p) < \epsilon$ for some ...
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Can we calculate entropy for nonstationary random variables?

Let's assume that $X$ is a discrete random variable, which can take any value from the set $\{x_0,\dots,x_n\}$ with the probability mass function $P(X)$. We can calculate the entropy of $X$ as ...
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Fano's inequality and error rate

The Wire-tap channel II (http://link.springer.com/chapter/10.1007%2F3-540-39757-4_5) article in proof of Theorem 1 uses Fano's inequality to estimate the entropy $H(S|\hat{S}) \leq K \cdot h(P_e)$ ...
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Comparing a vector with a sorted vector

I am currently looking at a database in which the tuples have a natural order in which each tuple has an integer ID that reflects its age. The more recent the tuple, the larger its ID. Now we are ...
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Huffman codes: does less entropy imply less weighted average codeword length?

Let $\Sigma$ be a source alphabet with a probability distribution over its symbols $P$. Then, the Shannon entropy of $\Sigma$ is $$-\sum p_j \times -\mbox{log}_2(p_j)$$ where $p_j$ is the probability ...
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How to prove the function is logarithmic with coefficience

I am given a set of properties for an unknown function $f(x)$. In particular, not constantly zero, not negative, additive and continues for any $x$ between 0 and 1. I am asked to show equivalence ...
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Subadditivity of Entropy

We define $H(X) = -\sum_{x}p_{x}\log p_{x}$ and relative entropy as $H(p(x)||q(x)) = \sum_{x}p(x)\log \frac{p(x)}{q(x)} = -H(X)-\sum_{x}p(x)\log q(x).$ Now we have to prove that $H(X,Y,X) + H(Y) \leq ...
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How many code words if average code length equals entropy

I've been given a proof of the following: If $q\geq2$, then there is a source $S$ with $q$ symbols, and an instantaneous $r$-ary code $C$ satisfying $L(C)=H_r(S)$ if and only if $q\equiv 1 ...
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Understanding conditional entropy intuitively $H[Y|X=x]$ vs $H[Y|X]$

I was trying to understand conditional entropy better. The part that was confusing me exactly was the difference between $H[Y|X=x]$ vs $H[Y|X]$. $E[Y|X=x]$ makes some sense to me intuitively because ...
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Are measures of information model specific?

Does an information measure for a signal do a better job if it assumes some things about the signal? For example: I have a digital stream of data, 0s and 1s coming at a clock rate $r$. What is the ...
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how can prove this statement?

According definition of Kullback–Leibler divergence, we have: $"k[f|g]=\int_{}^{} f(x)\log \frac{f(x)}{g(x)}\mathrm dx"$. Now How can I prove this statement:$$k[f|g]=k[f|w]k[w|g].$$ Thanks in advance. ...
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Solomonoff induction , Shannon Entropy, Kolmogorov Complexity.

If Expected Kolmogorov Complexity equals Shannon Entropy why can't Shannon Entropy be used as an approximation of Kolmogorov Complexity in Solomonoff Induction? Regarding Kolmogorov Complexity and ...
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toplogical entropy of general tent map

Measure theoretic entropy of General Tent maps The linked question made me wonder how to calculate the topological entropy of a general tent map. Let $I=[0,1]$ and $\alpha \in ( 0,1)$. Define $T: I ...
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Entropy of geometric random variable?

I am wondering how to derive the entropy of a geometric random variable? Or where I can find some proof/derivation? I tried to search online, but seems not much resources is available. Here is the ...
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Looking for a a measure-theoretic treatment of “differential entropy”

If $X$ is a discrete random variable, its entropy $H(X)$ is usually defined as something along the lines of $-\sum \def\P{\mathbb{P}}\P(x) \log_2( \P(x))$, where the sum ranges over all the possible ...
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Shannon Entropy Minimization

The Shannon Entropy for an observation is given by $ -x \log_2(x)$. Why is the maximum entropy achieved at $x = \frac{1}{e}$, and not at $x = 0$? Could someone provide a logical explanation that ...
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For P0 close to P1 the relative entropy can be approximated by its series expansion,Why?

I am reading a article (An overview of distinguishing attacks on stream ciphers, Martin Hell · Thomas Johansson · Lennart Brynielsson) about Distinguishe Attacks. There is a approximate equation ...
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Does entropy $H(y)$ decrease as $H(x,y)$ decreases when $H(x)$ is fixed?

Can't find any proof in Shannon's 1948 paper. Can you provide one or disproof? Thank you. P.S. $H(x)$(or $H(y)$) is the entropy of messages produced by the discrete source $x$(or $y$). $H(x,y)$ is ...
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Maximize the expected Brier Score of P relative to P.

Fix a finite set $\mathcal S$ and let $\mathcal P$ be the collection of probability functions over the Boolean closure of $\mathcal S$. Let $\beta : \mathcal P \times \mathcal S \to \mathbb R$, with ...