Questions tagged [entropy]

A mathematical quantity designed to measure the amount of randomness of a random variable.

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Is the generalized entropy of certain events alway null?

In their paper: Novel Decompositions of Proper Scoring Rules for Classification, Kull and Flach wrote in section 2.2 Divergence, Entropy and Properness that when $y$ is the true class $d(p,y)=\phi(p,...
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Name for a particular family of distributions on unit simplex [closed]

Let $\Delta^n$ be the unit simplex, and let $\alpha\ge 0$ be a parameter. Is there a standard name for the following probability density function supported on $\Delta^n$? $$f_{\alpha}(p)=C(\alpha)e^{-\...
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How to conceptualise statistical entropy

Preamble There's this great YouTube video explaining the entropy of a probability distribution using the idea of "what is the minimum number of questions I need to ask?". So as an example, ...
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Measuring entropy/concentration in multivariate datasets

I am interested in measuring how concentrated or widely dispersed a certain binary property is among a population, which is defined by a number of categorical variables. For instance, let's say I have ...
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Change in Shannon Entropy in Markov Chain Process

What are the known results on the change in Shannon entropy $\Delta H_{k} = H(\vec{p}_{k}) - H(\vec{p}_{k-1})$ of the $k$-th step in a process governed by a finite state discrete time Markov chain ...
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Does logit transformation of information entropy values make sense?

I have a vector of information entropy values that range between 0 and 1 which I want to explain with some explanatory variables. I realized that the distribution of the entropy values in my dataset ...
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Relationship between log-likelihood function and entropy (instead of cross-entropy)

Negative log-likelihood function $$ -\ln f(X\mid\Theta)=-\int_{-\infty}^{\infty}\ln f(x\mid\Theta) \, \mathrm{d}x$$ Differential entropy $$ h(X) = -\int_{-\infty}^{\infty} f(x) \ln f(x) \, \mathrm{d}...
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Constructing a joint distribution from pairwise marginals

Consider a set of random variables $\{X_i\}$ with joint pdf $f(x_1 ... x_n)$. Given the marginal pdfs $f_i(x_i)$, we can construct a joint distribution $$g(x_1 ... x_n) = \prod_i f_i(x_i)$$ which has ...
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Information Entropy Problem

I cannot figure out this simple entropy problem and it is driving me crazy! From McElreath's Statistical Rethinking: Imagine instead 5 buckets and a pile of 10 individually numbered pebbles. You stand ...
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“Information” Correlation

(Let $X$ and $Y$ be random variables, sufficiently nice for my question to make sense.) $$ \text{Correlation} $$ $$ \rho(X, Y) = \dfrac{\text{cov}(X, Y)}{\sqrt{\text{var}(X)}\sqrt{\text{var}(Y)}} $$ ...
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Information gain of the root node

Recently I saw this question and answer as attached in following image Anyone can add details how this solution achieved?
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Can a likelihood's relative entropy be related to its predictive accuracy?

Suppose I have some prior $\pi(\theta)$, from which I draw $N$ samples, each having parameter $\theta_i$. These $\theta_i$'s are known to me. Suppose that one of these samples (unknown to me which) ...
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Bounds on the difference between entropies of two distributions?

Have there been any results regarding bounds on $\left| H(\hat{\pi})-H(\pi) \right|$ where $\hat{\pi}$ and $\pi$ are the empirical and true probability distribution?
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Clarification of variational autoencoders

I've been spending almost a month trying to understand VAE. I was reading a bunch of tutorials, and first it made sense, and seemed straightforward. Then I was experimenting with it, it produced weird ...
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Measure of segregation that takes population-level proportions into account (modified entropy/Theil index)

I am interested in a measure of segregation which allows for multiple groups and is maximized when group levels reflect the population as a whole (rather than equality of group size). The best I have ...
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CNN: quantitatively evaluate the activation of a filter

When an image is fed into a CNN, it would pass through different layer of different filters. The visualization of these filters look like: Here we can safely claim that the 1st filter is more ...
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Entropy of multivariate Pearson Type II

I was checking the entropy of multivariate Pearson Type II for maximizing Renyi entropy. It would be great if someone came up with this question or advise some reliable sources.
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Minimising KL divergence between two distributions

say we want to approximate a distribution $p(x)$ with $q(x|\theta)$. We do not know the distribution $p(x)$ but we can draw samples from $p(x)$. The KL divergence between the two distributions is $$ \...
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Why doesn't the entropy decrease with an increasing number of observations?

I'm trying to think more about entropy. I have the following toy example: Consider a coin flip. Case 1: I think p_h = 0.5 The entropy of this is 0.5 ln(0.5) x 2 = ln(0.5) Case 2: I don't know what p_h ...
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Statistically, is it effective to design exams to get harder as you answer correctly?

I think we have all taken exams where as you answer more questions correctly, the exam gets harder. Intuitively, this is obvious why...If an IQ test had questions for 3rd graders, everyone would get ...
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Information gain for unequally sized time series

I have some time series data, $\mathbf{x}_{1:T} = \{ x_1, \dots, x_T \}$ where the observation at time $t$, $X_t$, is a continuous random variable. Let $Y_t$ denote a discrete random variable at time $...
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Is there a metric to identify whether samples are uniformly distributed when number of samples is small?

Suppose I have a small number of samples drawn from an unknown distribution $\{X_1,X_2,...,X_n\}$, where $0\le X_i \le L$, and $3\le n \le10$. I want to identify a metric to understand how far these ...
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entropy regularization in generative model

I am wondering if it is possible to use entropy as a regularization in a generative model. For example, in the conjugate model where $x_i \in X$ is observed data and generated from a Normal ...
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Log probabilities versus squared probabilities (entropy vs Gini)

The advantage of log probabilities over direct probabilities, as discussed here and here, is that they make numerical values close to $0$ more easy to work with. (my question, instead of the links, ...
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When would you use purity as a measure of external validity over entropy? [closed]

This question particularly pertains to text clustering. I've not really found anything on why one would use purity over entropy or vice versa. Could someone explain this to me?
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How does Information Gain work?

I know the algorithm upon which the information gain is based. But how are we sure that after a split, its entropy will always decrease i.e. on dividing the data into smaller chunks, the entropy will ...
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Maximum Entropy Discrete Distribution

In Pattern Recognition and Machine Learning the author uses Lagrange multipliers to find the discrete distribution with maximum entropy. Entropy is defined by; $$H=-\sum_i p(x_i)\ln(p(x_i))$$ and the ...
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What is the effect of number of points on calculation of mutual information between two variables?

I was trying to calculate mutual information between two variables as follows: mutual_info_score(x,y) This essentially creates a 2d histogram and evaluates the ...
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If entropy is the underlying measure for KL-divergence, what is the underlying measure for the Wasserstein distance?

If entropy is the basis measure underlying KL-divergence (aka relative entropy), what is the basis measure underlying the Wasserstein distance?
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If a zero entropy distribution implies high information a priori, what does it mean ex posteriori?

The following counteracts the statements made for the maximum entropy principle case in order to posit a pseudo "minimum entropy principle" case that is simply the polar opposite of the ...
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Truncated entropy

If we bound a continuous random variable's probability distribution from below with $a$ and above with $b$, would the differential entropy of this truncated portion of the pdf just be $$h(X)_{trunc} = ...
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Which has minimum concentration: the uniform distribution or the maximum entropy distribution?

For a continuous random variable, the uniform distribution has high entropy because it demonstrates the greatest level uncertainty. However, this conflicts with the maximum entropy principle, which ...
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Entropy of RV that is function of coin flips

Suppose that random variables $X_1,\dots,X_n$ correspond to independent and fair coin flips, i.e., it holds for their entropy that $H[ X_i ] = 1$, for all $i$. Let $Y = f(X_1,\dots,X_n)$ be some ...
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General implications of low entropy for a dataset

I am fairly familiar with entropy, which quantifies uncertainty/surprisal of a random variable. In my case, I have a corpus where I can use empirical word frequencies to estimate entropy of the entire ...
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Survival entropy

If continuous random variable $X$ has a probability distribution $f(x)$, the entropy of $X$ in its entirety (all of $f(x)$) can be calculated as $H(X)$. If I am more interested in measuring the ...
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What is the entropy of multivariate data multiplied by a vector?

It is a general rule that for multivariate data $\boldsymbol{X}$ and a matrix $\boldsymbol{A}$, their entropy is $$h(\boldsymbol{A} \boldsymbol{X}) = h(\boldsymbol{X}) + \ln |\det \boldsymbol{A}|$$ (...
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Entropy of large sample $X$ and small sample $Y$ comparison

Distribution $X$ of a real continuous random variable is unimodal and slightly non-normal with $1,000$ observations. Distribution $Y$ has the same characteristics (not perfectly identical in moments ...
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Is entropy the same for a shifted-mean distribution? [duplicate]

The image below shows two identically shaped (Normal) distributions with the second only different by its mean. If I calculate the differential entropy of both separately, would the entropies of the ...
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Can $f$-divergences narrow the discrepancy between train and test fits in machine learning?

Machine learning models whose task is to predict unseen test data would work best if the test data's distribution turns out to be the same as the training data's distribution. Real data seldom works ...
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What is the entropy of a riskless random variable?

Variance and standard deviation are often used as proxies for risk and volatility. I make the analogy to information theory as follows, correct if it's wrong: a random variable $x\in \mathbb{R}$ that ...
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How come differential entropy can be negative but mutual information can't?

Differential entropy, $$H(X) = -\int_{-\infty}^{\infty} p(x) \ln p(x) dx,$$ ordinarily is positive (the negative sign in front actually makes the entire expression positive). However, it can be ...
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For real variables, variance is to entropy, what the mean is to -?

If $X$ is a real random variable with a pdf, variance/standard deviation is a measure of $X$'s dispersion about the pdf's central tendency, which in turn is referred to as the mean of $X$. For many, ...
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Is conditional entropy noise entropy?

Can the conditional entropy, $H(X|Y)$, of two random variables $X$ and $Y$ be thought of as noise entropy in the formula for mutual information, $$I(X,Y) = H(X) - H(X|Y)$$ making $I(X,Y)$ the full ...
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Do identical entropies imply perfect correlation?

If two random variables $X$ and $Y$ have the same Shannon entropy, $$H(X) = H(Y)$$ can it be said that their Pearson correlation $\rho(X,Y) = 1$? Is it true always or just in some cases, which cases
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Can two variables' entropies be equal, as well as their joint entropy?

If two random variables $X$ and $Y$ have the same Shannon entropy, $$H(X) = H(Y)$$ can their joint entropy ever be equal to both? $$H(X,Y) = H(X) = H(Y)$$
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Mutual Information and the informational coefficient of correlation

Linfoot (1957) introduced the informational coefficient of correlation, $\mathit{IC}$: $$IC=\sqrt{1-e^{-2\cdot{I(X;Y)}}}$$ Where $\mathit{I(X;Y)}$ is the mutual information, the measure of the shared ...
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Entropy regularization versus L2 norm regularization?

In multiple regression problems, the decision variable, coefficients $\beta$, can be regularized by its L2 (Euclidean) norm, shown below (in the second term) for least squares regression. This type of ...
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How does a distribution's differential entropy correspond to its moments?

The Gaussian distribution maximizes entropy for the following functional constraints $$E(x) = \mu$$ and $$E((x-\mu)^2) = \sigma^2$$ which are just its first and second statistical moments (true ...
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Is KL-divergence just the multiplication rule for independent events, reformulated in terms of entropy?

We know KL-divergence is sometimes expressed like this: which shows it's capturing the deviation between the joint distribution of X and Y, and the product of marginals for X and Y. This suggests KL-...
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How to measure whether a discrete distribution is uniform or not?

Say I have two vectors [1,2,1,2,2] and [1,2,1,1,1]. The number at each dimension is the frequency of one element. How do I measure whether these two vectors are close to the uniform distribution? I ...

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