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

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Is there a connection between topological entropy and stationary distributions?

In a book I read the following: "The topological entropy is the supremum over all stationary distributions of the entropy of the corresponding stationary sequence." I did not find this definition ...
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31 views

Mutual Information: Are these two equations equal?

I'm working with Multivariate Mutual Information (MMI), specifically with three variables $(X,Y,Z)$, applied to RNA sequences. The MMI equation that I use for three variables is based on entropy ...
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197 views

Relative entropy between singular measures

Usually, to define relative entropy between two probability measures, one assumes absolute continuity. Is it possible to extend the usual definition in the non absolutely continuous case?
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46 views

Approximation for a binomial coefficient sequence summation

What is a good approximation to $$\dfrac{{\binom{k}{i}}{\binom{k}{i}}(i-1)!}{\binom{k(k-1)/2}{i}}$$ $$\dfrac{{\binom{k}{i}}{\binom{k}{i}}(i-1)!}{(2^{(\log ...
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23 views

How to calculate entropy from a set of samples?

entropy (information content) is defined as: $$ H(X) = \sum_{i} {\mathrm{P}(x_i)\,\mathrm{I}(x_i)} = -\sum_{i} {\mathrm{P}(x_i) \log_b \mathrm{P}(x_i)} $$ This allows to calculate the entropy of a ...
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27 views

Partition-based entropy of a sequence

The entropy $H$ of a discrete random variable $X$ is defined by $$H(X)=E[I(X)]=\sum_xP(x)I(x)=\sum_xP(x)\log P(x)^{-1}$$ where $x$ are the possible values of $X$, $P(x)$ is the probability of $x$, ...
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19 views

How to efficiently select a subset of elements that maximizes a certain property? (entropy)

I need to select $k$ elements from a pool containing a much larger number $N$ of elements. The selection must be done in a way that a function $h(\{z_{i_1},\ldots,z_{i_k}\})$ is maximized or ...
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44 views

“Empirical” entropy.

Information entropy is usually defined as $$\text{I}({\bf p}) = -\sum_{\forall i}p_i\log(p_i)$$ i.e. the expected value of the negative logarithm of the probabilities. This is all good when we have ...
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47 views

Fano's Inequality Proof

For an information theory class, I am studying the proof for Fano's inequality, i.e.: $H(P_e) + P_elog(|X|) \geq H(X|\hat{X}) \geq H(X|Y)$ Where $H(X)$ is the entropy of the random variable X ...
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402 views

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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1answer
42 views

Is conditional entropy ever taken to be a random variable?

In probability theory, the conditional expectation $E(X|Y)$ and variance $V(X|Y)$ er usually taken to be random variables, st. the value of $E(X|Y)$ depends on what value $Y$ ends up taking. I've ...
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39 views

Topological Entropy of $T$ on subset $Y\subset X$

Let $X=\left\{0,1,2\right\}^{\mathbb{Z}}$ and on it the following dynamics described by $T\colon X\to X$ as follows: A 1 becomes a 2, a 2 becomes a 0 and a 0 becomes a 1 if at least one of its two ...
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1answer
18 views

How does a wavelet help in compressing data

I have an understanding of how we carry out image compression by using DCT along with Huffman encoding. The next subject is wavelets. I understand that wavelets are small waves and there are different ...
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49 views

Shannon entropy on the simplex

The state of a trit $$\{p_1,p_2,p_3=1-p_1-p_2\}$$ can be represented as a triangular simplex. The centre of the simplex is the maximally mixed state $$m=\{\frac{1}{3},\frac{1}{3},\frac{1}{3}\}$$. And ...
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35 views

Shannon Entropy Maximization with Constraints

I have got a cumulative distribution function $F_X(x)=Pr(X<=x)$. This distribution is described by 2 parameters $\alpha, \beta$. We define $F_k$ as follows: $\forall k<=n_k, ...
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20 views

Topological entropy and degree of smooth mappings

Where can I find the literature "Topological entropy and degree of smooth mappings" by Misiurewicz. Thanks for any help.
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58 views

Given a Markov chain $X \rightarrow Y \rightarrow Z$, why is $I(X;Y|Z) \leq I(X;Y)$?

A Markov chain $X \rightarrow Y \rightarrow Z$ is given, where $X,Y,Z$ are random variables characterized by the probability distribution $p(x,y,z) = p(x)p(y|x)p(z|y)$. It follows that $I(X;Y) \geq ...
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1answer
34 views

Clarifying Derivation of Entropy

I'm learning about probability from the book Pattern Recognition and Machine Learning by Christopher Bishop. It includes a justification for the definition of entropy that can be summarized as: let ...
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1answer
291 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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1answer
28 views

Mutual Information: How these two equations are equal?

I'm a biologist trying to apply the Mutual Information (MI) to some RNA secondary structure. I know that there exists two MI equation that, mathematically, are equal: $I(X,Y) = \sum_{x,y} p(x,y) ...
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25 views

The difference in entropy rates between a hidden process and its observation

Let $S$ be a finite state space and $o:S\to S$ an observation function. Given a distribution $p$ on $S\times S$, consider the following optimization problem: $$\max \left[ EntropyRate(\{x_t\}) - ...
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1answer
27 views

Infinite closed shift-invariant set of binary sequences with zero entropy?

Does there exist an infinite closed shift-invariant $X \subset \{0,1\}^\Bbb Z$ with zero topological entropy? How to think of an example? Will periodic points of shift$|_X$ have zero entropy?
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36 views

Derivation of equation for self information

I am trying to understand how the formula I(x) = -log(p(x)) for self information was derived. From what I have read, 2 constraints were imposed on the properties ...
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378 views

Does $\operatorname{div}\left(\nabla G +xG\right)=0\Longleftrightarrow \nabla G +xG=0$?

Let $G$ be a smooth function defined on $\textbf{R}^d$. What are the assumptions I should use to assume that $$\operatorname{div}\left(\nabla G(x) +xG(x)\right)=0 \quad (\forall x\in \textbf{R}^d)$$ ...
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37 views

$\log (A + \delta A) = ?$ (as an expansion in $\delta A$), where $A$ and $\delta A $ are matrices

$A$ and $\delta A$ are two non-commuting matrices and I am seeking a power series expansion to 2nd order in $\delta A$. After writing it as $\log (A (1 + A^{-1}\delta A) )$, I am unable to figure out ...
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Graph Entropy - A Tractable Measure to Measure Distinguishability of Neighbourhoods

Given a labelled directed graph G, I am interested in a measurement of G that captures how distinguishable arbitrary connected sub graphs of G are. Labels may repeat and as such two or more different ...
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10 views

How to define a one-parameter family of probability distributions

I am trying to evaluate a noise-source as a means of providing entropy to a random number generator. I am running into trouble when it comes to determining the probability distribution that has the ...
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1answer
61 views

What is the maximum entropy distribution over all integers (ie. including negative ones) with fixed mean and variance?

I know that the maximum entropy distribution with over the non-negative integers fixed mean is a geometric distributions. However, I cannot find conclusive information about what are the maximum ...
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2answers
40 views

How much information does learning this interval give you?

Let's say you have a number $x$, and a priori, you know that $x \in [0, 1)$ (each value from 0 to 1 is equally likely.) Then a wizard comes and tells you that $x \in [a, b) \subseteq [0, 1)$. How much ...
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62 views

mutual information adds along path

Is it true that $I(X;Y)+I(Y;Z)=I(X;Z)$ for $X \to Y \to Z$? $I(X;Z) = H(X)+H(Z)-H(X,Z)$ and $I(X;Y)+I(Y;Z) = H(X)+H(Z)-H(Z|Y)-H(X|Y)$ Hence, we would require $-H(X,Z)=-H(Z|Y)-H(X|Y)$ -- is it true? ...
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Scalar derivative of ${\rm tr}~[A(x)\log A(x)]$ where $A(x)$ is a square matrix

How do i proceed to calculate $$\frac{d}{dx}{\rm tr}\left[{A(x) \log A(x)}\right]$$ where $A(x) \in \mathbb{M}(n)$ and $x \in \mathbb{R}$? The $\log$ function is the one defined by the exponential ...
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64 views

How many bit-flips are required to achieve random distribution?

If I have a binary number W bits wide, initially all set to zero, and I repeatedly pick a random bit and toggle it from zero to one or vice versa, how many times would I need to do this to achieve ...
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14 views

Matrix entropy measure

I have a matrix (its dimension is $n$ x $m$) where each cell can be $0$ or $1$. I would like to calculate an "entropy" measure on it that tells me how close are the ones together or how spread they ...
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56 views

Entropy of a Markov chain (right result?)

Consider the Markov chain with state space $E=\left\{0,1,2,3,4,5,6\right\}$ and transition matrix $$ \begin{pmatrix}1/5 & 3/5 & 0 & 0 & 1/5 & 0 & 0\\0 & 0 & ...
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Markov chain and conditional entropy [closed]

Markov chain (DTMC) is described by transition matrix: $$\textbf{P} = \begin{pmatrix}0 & 1\\ \frac{1}{2} & \frac{1}{2} \end{pmatrix}.$$ Initial distribution $X_1 = \left(\frac{1}{4}, ...
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1answer
44 views

For $A,B,C$ independent and normal, what is $I(A+B;\ A+C)$?

Say $A,B,C$ are mutually independent and normally distributed with zero mean but possibly different variances $\sigma_1,\sigma_2,\sigma_3$. What is the mutual information between $A+B$ and $A+C$? All ...
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1answer
68 views

Topological Entropy of $T$, on a disjoint union?

Let $X$ be a compact metric space and $T\colon X\to X$ continuous. By $h(A\cup B\cup C,T_{|A\cup B\cup C})$ denote the toplogical entropy of $T$, restricted on $A\cup B\cup C$, where $A,B,C\subset X$ ...
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1answer
42 views

Shannon entropy and inequality of expectations

Consider two distinct probability distributions $P(X)$ and $Q(Y)$---defined on the same domain---with (Shannon) entropy of $H(X)$ and $H(Y)$. I am interested to prove (or disprove) that $$ H(X) \leq ...
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10 views

Which argument in KL Divergence minimization?

The KL divergence $D_{KL}(p||q) = p^T\ln(\frac{p}{q})$ is not a distance measure because first of all it is not symmetric. In applications, one usually has a prior distribution, say $y$, and wants ...
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27 views

Logarithm of Probability measure of a set

What does the parameter K immediately suggest? Suppose we have a non-uniform probability measure $Q$ on a set of sequences of length $n$, $A$, and ${Q^n}$ is the corresponding product measure. $K = ...
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37 views

Shannon entropy property proof

X and Y are two discrete random variables having $n$ possible values : $x_{i}(1\leq i \leq n)$ and $y_{j} (1\leq j \leq n)$. The probability mass function of X is given by $$ Pr(X=x_{i}) = p_{i}, ...
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Are there any other measures of complexity for a continuous map than topological entropy?

Let $X$ be a compact topological space and $T\colon X\to X$ be continuous. In order to say something about the complexity of $(X,T)$ there is of course the notion of topological entropy of $T$, ...
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43 views

Entropy of $\operatorname{Beta}(\alpha, \beta, a, c)$

I know that the differential entropy of the two parameter Beta distribution $X \sim \operatorname{Beta}(\alpha, \beta)$ is $$ \begin{align} h(X) = \ln \operatorname{B} (\alpha, \beta) &- ...
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1answer
22 views

Can topological entropy be infinte?

I wonder if the topological entropy as defined by Adler or Bowen can be infinity. Can you answer that?
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12 views

Entropy of a 2D function versus 1D function.

I am a novice in information theory so this is more of a question seeking pointers to ideas/references to think further on the thought. I want to make concrete the idea that a function of two ...
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1answer
235 views

Non-zero Conditional Differential Entropy between a random variable and a function of it

Let two continuous random variables, where the one is a function of the other: $X\, $ and $\, Y=g\left(X\right)$. Their mutual information is defined as ...
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1answer
24 views

Decomposing factorized entropy

I am trying to figure out how the equation for factorized entropy below is derived. The equation for entropy is $H(Q) = -\sum_x Q(x)\log Q(x)$ where $Q$ is a probability distribution. Let $Q(x) = ...
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1answer
18 views

How to prove the inequality on relative entropy?

Here is the definition of Relative Entropy Now I am only interested in the simplest condition that the index set is finite and discrete, as the naive probability distribution vectors. Now if the ...
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1answer
44 views

Joint entropy calculation of discrete random variables

Suppose that i want to calculate the joint entropy $H(A,B)$ of two discrete random variables of the form: $A=\{-1,1,1,-1,-1,-1,1,1\}$ and $B=\{1,-1,1,1,-1,-1,-1,1\}$. If the goal was just the ...
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1answer
77 views

Is there a symmetric alternative to Kullback-Leibler divergence?

I have two samples of probability distributions that I would like to compare. I have previously heard about the Kullback-Leibler divergence, but reading up on this it seems like its non-symmetricity ...