The science of compressing and communicating information. It is a branch of applied mathematics and electrical engineering. Though originally the focus was on digital communications and computing, it now finds wide use in biology, physics and other sciences.

learn more… | top users | synonyms (1)

5
votes
2answers
113 views

Determinant of Fisher information

In information geometry, the determinant of the Fisher information matrix is a natural volume form on a statistical manifold, so it has a nice geometrical interpretation. But what is it in ...
2
votes
2answers
52 views

$H(X\mid Y_1, Y_2) \leq H(X\mid Y_1)?$ (Conditional Entropy with conditioning on multiple RVs)

In short, my question is whether the "conditioning reduces entropy" maxim is also true when conditioning on one random variable as compared to conditioning on two: $$H(X\mid Y_1, Y_2) \leq H(X\mid ...
0
votes
1answer
73 views

Is it possible to study information theory while studying a first course on probability?

I'm currently taking a course on intro to probability. The course is not mathematically rigorous and does not invoke theorems from real analysis, etc. The course covers all the way from basic ...
2
votes
1answer
40 views

Self-information, one event half as likely than another event conveys twice the amount of information?

I was reading the following: "If one event is half as likely as another, then learning about the former event shouldconvey twice as much information as the latter" I know it should be easy to ...
0
votes
1answer
54 views

How is this paper using probability notation?

I am trying to understand this paper about documents and sentences. At the end of page three, they say: Let g(wi, wj ) be the distance between two events (1 if in the same sentence, 2 in neighboring, ...
0
votes
1answer
45 views

Erasure Codes with Simplex Locality

In here, theorem $1.1$. there is this line 'Since $G$ has full rank it is possible to enlarge $N$ to a set $N^{'}$ ... exactly $k-1$. Note that the enlargement operation ... any of the leaders' in the ...
-2
votes
1answer
64 views

Analysis of Kullback-Leibler divergence

Let us consider the following two probability distributions ...
1
vote
2answers
224 views

Upper bound on the entropy of a sum two random variables

Let $X$ be a random variable such that $|X| \leq A$ almost surely, for some $A > 0$. Let $Z$ be independent of $X$ such that $Z \sim {\cal N}(0, N)$. My question is: How large can the entropy ...
0
votes
1answer
44 views

mutual information of coupled variables

I have been looking for a method for evaluating the mutual information between a combination of source variables, $X_0, X_1$ and a target variable, $Y$. $$I(Y;X_0,X_1)$$ When I look on wikipedia's ...
1
vote
2answers
54 views

Ways to code two arbitrary binary strings into one without loss of information, and relevant bounds

If the title was not clear, I'm examining methods of taking two binary strings as input and outputting one binary string in such a way that the two original strings can be extracted from the output, ...
2
votes
1answer
64 views

partition with infinite entropy?

Let $P$ be an infinite partition of the interval $[0,1]$. Let $P$ have elements $I_i$ which has Lebesgue measure $m(I_i)$. Then the entropy of $P$ is defined by $\sum_i -m(I_i)\log m(I_i)$. Can this ...
1
vote
0answers
22 views

Mutual information staying constant under composition of channels

Consider the following scenario: one has 2 communication channels $C_1$ and $C_2$. Let $p_0(x)$ be some arbitrary but fixed input probability distribution. The mutual information between the input ...
0
votes
0answers
57 views

Special case of Kullback-Leibler additivity

I have three random variables $X,Y,Z$. If $(X,Z)$ are an independent pair and $(Y,Z)$ are an independent pair, then the additive property of the Kullback-Leibler divergence says $K(X,Z|Y,Z) = K(X|Y) ...
2
votes
1answer
37 views

Confusion about non-negative mutual information

The formula I was given for calculating information for a specific stimulus $s_x$ is: $$I(R,s_x) = \sum_i p(r_i|s_x) \log_2{p(r_i|s_x)\over p(r_i)} $$ It was also said that information is always ...
7
votes
0answers
250 views

Entropy of matrix vector product

Consider a random $n$ by $n$ circulant matrix $M$ whose entries are chosen independently and uniformly from $\{0,1\}$. Let $M'$ be the $m$ by $n$ matrix which is formed by taking the first $m$ rows of ...
7
votes
3answers
187 views

Using decimals of $\pi$ to store data

I read recently about an idea to, instead of storing actual data, converting the data to a string of digits and then store the index of where this pattern occurs in some number, for example $\pi$. The ...
3
votes
1answer
115 views

Upper Bound on Mutual Information

I am interested in an upper bound on mutual information that I have been encountering frequently in the statistics and probability literature. I have yet to see the "purest" form of the inequality, so ...
3
votes
0answers
69 views

Mutual Information for Gaussian Process (and also Fano's Inequality)

According to this presentation: Bounding Gaussian Process Information Gain we have a closed-form expression for the information gain as follows: $$ I\left(\vec{y} \mid f\right) = \frac{1}{2} \log\det ...
1
vote
0answers
29 views

Do 2 timeseries represent the input better than one?

I only have a very basic familiarity with signal processing and information theory so I'm sorry if this is a very straight forward question. I have a very brief input signal and two timeseries as ...
3
votes
1answer
100 views

Relationship between two measures of inequality

Let $A = \{a_1,\dots,a_n\}$ be a multi-set and let $B$ be the set of distinct elements in $A$. Now define $H(A) = -\frac1n \sum_{x \in B} f(x) \log_2(f(x)/n)$ where $f(x)$ is the number of times $x$ ...
0
votes
1answer
44 views

Bounds for Mutual Information

$V_1$, $V_2$ be two binary strings with equal number of bits (say the length is $l$). Then the mutual information of $V_1$, $V_2$ can be defined as: $I(V_1;V_2)$ = $\sum_{y \in Y} \sum_{x \in X} ...
5
votes
2answers
227 views

How to Count the number of words over an alphabet subject to restrictions on letter count?

For an alphabet $X$, is there a method of computing how many words over $X$ of length $n$ there are where the number of occurrences of each letter must satisfy a system of equations? For example if ...
1
vote
2answers
95 views

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 ...
1
vote
1answer
112 views

Calculate Huffman code length having probability?

Having an alphabet made of 1024 symbols, we know that the rarest symbol has a probability of occurrence equal to 10^(-6). Now we want to code all the symbols with Huffman Coding. How many bits ...
0
votes
1answer
283 views

Using binary entropy function to approximate log(N choose K)

I am not a mathematician and struggling with the exercises while reading this book Information Theory, Inference and Learning Algorithms. The author introduced the binary entropy function at the ...
1
vote
1answer
305 views

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 ...
0
votes
1answer
64 views

Equality of Information Gain and Mutual Information

I am curious about definition of information gain and mutual information in the context of feature selection. If looks like two these measures define exactly the same thing, however I didn't find ...
2
votes
0answers
54 views

Shannon-Fano analysis, Binary-search-like

Prove that the codewords of the Shannon-Fano code satisfy $l_i \leq \left \lceil \log _2 \frac1{p_i}\right \rceil$. Elementary wording: given positive numbers in descending order $p_1,...,p_n$, ...
0
votes
1answer
35 views

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 ...
5
votes
1answer
64 views

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)] ...
5
votes
1answer
176 views

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 ...
2
votes
1answer
77 views

Is there a combinatorial explanation for this identity related to Kraft's inequality?

Kraft's inequality involves the quantity: $$\sum_{x \in X} \frac 1 {b^{\ell(x)}} \tag 1$$ Where we are considering a code mapping symbols in the alphabet $X$ to strings in an alphabet of $b$ ...
0
votes
3answers
1k views

Similarity between two probability distribution

I am not sure how to put the question. I am not even sure if this question makes sense at all. I know that the similarity of two discrete (or continuous) distributions can be quantified by ...
0
votes
1answer
62 views

A question on Markov chain

Suppose for two random variables $X$ and $Y$ we have $X\perp\!\!\!\perp Y$ and also assume that three random variables $X$, $Y$ and $Z$ form the following Markov chain: $X\to Z\to Y$. Do these two ...
0
votes
1answer
83 views

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: $$H(Z)=H(X)+H(Y)$$
2
votes
0answers
67 views

Good low-rate, short-length block codes

I am highly unsure whether this question is appropriate for this site (as it is at no point a math problem), yet searching in the stackexchange universe for similar topics showed the most hits on ...
1
vote
0answers
51 views

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 ...
3
votes
0answers
26 views

Entropy of Group Action by Knowing Finiteness of Unidimensional Subaction

I've been trying to solve the following problem " Considering a measurable dinamical system $(X, \mathcal{B}, \mu, \mathcal{T})$ where $\mathcal{T}$ is an action of a semigroup $G = N^d$ on $X$ for ...
0
votes
0answers
53 views

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 ...
2
votes
0answers
48 views

Is there an information theory for continuous time signals?

Information theory books talk about entropy and mutual information of discrete time processes, such as a sequence of symbols sent with a symbol period $T_s$ and there received sequence. Can we talk ...
-6
votes
2answers
126 views

How is Goedel's 1st incompleteness theorem related to the Axioms of a theory [closed]

i am thinking of various connections and formulations of Goedel's 1st incompleteness theorem. Apart from connections to Turing's Halting Problem and Algorithmic Complexity Theory, i am looking for ...
3
votes
0answers
74 views

Doubts in Bayes' Theorem

I meet one problem on the probability and statistic theory. "Assume given the probability spaces $(X,S,\mu_i)$, $i=1,2$, and the probability space $(X,S,\lambda)$. And there exsit functions ...
1
vote
1answer
127 views

Mutual Information in an Binary Erasure Channel

Imagine a Binary Erasure Channel as depicted on Wikipedia. One equation describing the mutual information is following: $$\begin{array}{rcl} I(x;y) &=& H(x) - H(x|y) \\ &=& ...
0
votes
1answer
49 views

Calculating Entropy of Dependent Random Variables

So basically I'm trying to answer the following exam problem: I'm half struggling on H(Z | X) and H(X | Z) and mainly just need confirmation. I know that H(Z | X) = -SUM P(Z|X)P(X)logP(Z|X) ...
0
votes
1answer
57 views

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 ...
2
votes
2answers
47 views

Does X|Y = X formally, in the sense of RVs?

In Cover and Thomas' "Elements of Information Theory", the joint entropy $H(X,Y)$ is defined, but they state that this definition is nothing new if we consider that it is the entropy of a single ...
1
vote
0answers
40 views

Help understanding KL-Divergence

I will be doing a course in Information Theory soon and to get some early learning in I have been attempting a question with a joint probability mass function represented by the following table: In ...
2
votes
1answer
32 views

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 ...
3
votes
2answers
101 views

Bounding second moment of entropy

Entropy is defined as $E(-\log(P(x))$. We know it is bounded by $\log(r)$ when $r$ is the size of alphabet. Defining the second moment as $E(\log^2(P(x))$, how to show it is bounded?
0
votes
1answer
57 views

How does the presenter in this video derive this formula?

I am watching this coursera video on entropy (in the information theory sense of the word). Right around the two minute mark the presenter shows two forms for H(p). The first (after the equals sign) ...