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

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Topological entropy and orbit complexity

Let $X$ be a compact metric space and $T\colon X\to X$ be continuous. There is a connection between the Kolmogorov-Sinai entropy $h_{\mu}(T)$ and the orbit complexity $K(x,T)$ of $x\in X$ as follows: ...
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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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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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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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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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22 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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“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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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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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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34 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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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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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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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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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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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
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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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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$\log (A + \delta A) = ?$ (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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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Calculating Entropy

Hi there kind people, I'm studying for an Artificial Intelligence test in a week or so, and this question is from a past paper - and it has really stumped me. Any help would be appreciated. Thank ...
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23 views

The relation between the entropy of random variables $X$ and $Y=g(X)$

A previous post has shown that for random variables $X$ and $Y=bX$, where $b > 0$, the entropy of $X$ and $Y$ are not equal (Entropy of $Y=bX$). However, wouldn't any bijection $g$ on a random ...
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Entropy when left- and right shift move onto each other?

I do not know how to ask my question precisely but I try. If I have a finite alphabet $$ A=\left\{0,...,n-1\right\} $$ and then consider the space $$ A^{\mathbb{Z}} $$ with the left shift. Then for ...
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Entropy Formula $\sum_i p_i log(\frac{1}{p_i})$

In my algorithms course I have been introduced to the concept of entropy and data compression, mainly using huffman encoding. I am trying to understand the formula for entropy $$\sum_i p_i ...
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How to know the result of entropy function using uniform distribution set

In the entropy function here $H(s) = -\sum P(class=i|S)log_2{P(class=i|S)}$ I am trying to understand what is the domain of it's output for any input. I know that given a set where the frequency of ...
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How to choose assymetry for KL divergence?

I have two 2D probability distributions of eye movements of two different images. Suppose I call the first distribution of Image 1: $P$, and the second distribution of image 2: $Q$. Since ...
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Is the topological entropy of the “caterpillar waves” 0?

Please let me first describe the general background. The state of the system at time $t$ will be described by a scalar or phase $u=u^t$. Both $t$ and $u$ are discrete. $u$ take values from ...
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Interpreting Entropy

All you data scientists will probably know the entropy equation: $$H(p)=-\sum_{i=1}^{n}{{p}_{i}}\cdot\log_{2}{{p}_{i}}$$ And, using this, I was messing around with some compression, and calculated ...
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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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Question about conditional entropy.

Jointly distributed random variables (a) and (b) are distributed on the n-element set. Let $\varepsilon = Prob(a \ne b)$ It is needed to prove that $H(a|b) \le 1 + \varepsilon log(n-1)$. I tried ...
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Proof of an inequality with entropy and mutual information.

Entropy of a random variable (a) is (h) : $H(a) = h$. Mutual information of (a) and (b) is (3h/4) : $I(a;b) = 3h/4$. Mutual information of (a) and (c) is (3h/4) : $I(a;c) = 3h/4$. It is needed to ...