Questions tagged [kullback-leibler]

An asymmetric measure of distance (or dissimilarity) between probability distributions. It might be interpreted as the expected value of the log likelihood ratio under the alternative hypothesis.

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What is the KL divergence of distribution from Dirac delta?

The Kullback–Leibler (KL) divergence of two continuous distributions $P(x)$ and $Q(x)$ is defined as $$D_{KL}(P \mid\mid Q) = \int_{X} P(x) \log{\left[\frac{P(x)}{Q(x)}\right]} dx$$ How can one ...
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Asymmetry of the Kullback-Leibler distance in hypothesis testing

My question is related to the asymmetry of the Kullback-Leibler distance. I'm using the discrete definition of the Kullback-Leibler distance, so we have: $$ KL(p,q) = \sum_{s \in S} p(s) \log\left( \...
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117 views

Orthogonal intersection in a Riemannian manifold

Let $S$ be the set of all probability distributions on $\mathbb{R}$ and $S_n=\{p_\theta\}$ be an $n$ dimensional submanifold of parameterized family of probability distributions on $\mathbb{R}$ where $...
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Estimating parameters using Kullback-Leibler or Kolmogorov-Smirnoff via Nelder-Mead

I want to find the parameters of a model which specifies a set of classification probabilities, for say M classes. (I'll use the parameters in another model later.) Given a set of parameters $\theta$,...
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Lomax distributions - Kullback Leibler divergence

Does anyone know of a reference for an expression for the Kullback-Leibler divergence between two Lomax (Pareto II) distributions? Not really worried which way the Lomax is parameterized.
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Connection between log predictive density and Kullback-Leibler information measure

I have read in Bayesian Data Analysis by Andrew Gelman that log predictive density can be used to compare Bayesian models due to its connection to the Kullback-Leibler information. The log ...
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857 views

Maximum Likelihood & Bayesian inference minimizing Kullback-Leibler divergence?

I have heard/read that Bayesian and Maximum Likelihood inference can be justified as asymptotically minmizing the KL divergence between the pdf $p(x)$ actually describing the data and the ...
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Expected ratio of probabilities--is there a term for it?

I recently came across the following quantity when I played around with some information theoretic quantities and Bayesian learning. Given three probability distributions $q(z), p(z)$ and $p(z|x)$. $$...
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Quantify the information lost given by the Kullback-Leibler divergence measure

Consider there are $N$ individuals and these measure a quantity $X\in \mathbb{R}^{N\times M}$ where $M$ is the number of measurements and let $P(X)$ denote a probability distribution over $X$. The ...
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Cramer-Rao type bound for Information Gain

I am interested in the Bayes risk of some distribution $\pi$ $$ r(\pi) = \mathbb{E}_{\pi(x)}[ \mathbb{E}_{\Pr(y|d,x)}[L(x,\hat x(y|d))]], $$ where $L$ is some loss function and $\hat x$ is the ...
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129 views

Does this bounded, continuous probability distribution have a name?

Does this bounded, continuous probability distribution over $x$ have a name? $P(x|y) \propto \big(\frac{y}{x}\big)^x\big(\frac{1-y}{1-x}\big)^{(1-x)}$ for $x, y \in (0,1)$. This comes about by ...
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What is the distribution G (with given mean and variance) that has the minimum KL distance from normal distribution F?

I was hoping someone knows whether the following statement is true or not. Suppose $F$ is a given normal distribution and $G$ is a distribution that has a given mean $\mu$ and variance $\sigma^2$ (...
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How to use Kullback-Leiber divergence for discriminating 2 keywords

I'm getting 500 tweets for each of the keywords K1 and K2 and I want to discriminate these 2 keywords with Kullback-Leiber Divergence formula. I do normal text mining preprocessing such as removing ...
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80 views

Properties of Average Multinomial Likelihood

I am trying to understand the Kullback-Leibler Information: I read in http://arxiv.org/pdf/1404.2000v1.pdf the following: Ideally, we want the probability to be invariant to the number of ...
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Name of an $f$-divergence

The term divergence means a function $D$, which, given two probability distributions $P,Q$, assigns a non-negative real number $D(P,Q)$ such that $D(P,Q) = 0$ iff $P(x)=Q(x) \forall x$. The relative ...
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Properties of the KL topology [reference request]

I'm trying to understand better what are the implications of a sequence of random variables $X_n$ converging toward some limit $X$ in the KL topology, ie the probability density functions are such ...
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How to use KL-divergence in naive bayes classifier to weight features?

I have a dataset consisting of 4 classes. I have implemented the Gaussian Naive Classifier (in Matlab). In the training phase I calculate the mean and variance for each feature and each class as well ...
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Under what conditions will Kullback-Leibler divergence/mutual information be infinity?

For two perfectly correlated Gaussian variables, the mutual information between them, and thus the KL divergence between the product of the marginal distributions and the joint distribution, is ...
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Formal statistical test for comparing likelihood distributions obtained via MCMC

I am trying to formally compare the distribution of the likelihood values generated using two different models with marginal posterior values of the parameters obtained using MCMC in order to assess ...
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KL divergence between 2 distributions with unequal cardinalities?

Say $X$ is a discrete random variable with cardinality $|X|$ and $Y$ is a discrete random variable with cardinality $|Y|$. Does it make sense to talk about the KL divergences $D_{KL}(X||Y)$ or $D_{...
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Sample distribution for Kullback-Leibler distance

For two $n$ dimensional multivariate normal distributions $X_{1}\sim N\left(\mu_{1},\Sigma_{1}\right)$ and $X_{2}\sim N\left(\mu_{2},\Sigma_{2}\right)$, the Kullback-Leibler distance is given by $$KL=...
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What's good about I-projections?

There seems to be a large body of applied research where distribution q is picked to minimize KL(q,p) where p is empirical distribution. Are there theoretical reasons to prefer this estimator? For ...
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Relation Between Wasserstein Distance and Relative Entropy

Consider the Wasserstein metric of order one $W_1$ (aka the Earth Movers Distance). I would like to know whether it is possible to link $W_1$ and relative entropy and what this would mean intuitively. ...
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What is the relation between ELBO and SGVB?

Evidence lower bound (ELBO) can be minimised, so that to find the most appropriate approximative distribution of the target distribution, which is equivalent to the maximisation of the corresponding ...
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Name/definition of $\int \log F(x) \cdot g(x)dx$?

We know that: $$-\int \log f(x) \cdot g(x)dx,$$ where $f$ and $g$ are density functions, is known as the cross entropy. Does $$-\int \log F(x) \cdot g(x)dx,$$ where $F$ is the cumulative ...
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If the AIC is an estimate of Kullback-Leibler Divergence, then why can AIC be negative when KL divergence is always positive?

I have read many times that the AIC serves as an estimate of the KL divergence, and I know that AIC can be a negative value (and have seen that myself). Yet, the KL divergence must always be positive. ...
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571 views

Upper bound on KL divergence

Is there a maximum (unique?) to the KL divergence between discrete distributions p & q, with the restriction that q is a proper probability distribution? I know KL is unbounded from above when q ...
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Distance or divergence for ordinal distribution

Measures like KL divergence can be symmetrized (into JS divergence). Bhattacharyya distance serves a similar function. Either is well-suited to both continuous distributions and discrete (e.g. ...
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174 views

KL divergence and anomaly detection

stats newbie here. I have a dataset that is collected weekly. In order to make sure the data set gathered this week conform to past observations, I'm using KL divergence to compute how similar the ...
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Inequality Kullback divergence

I have a problem with solving the following question. Let $\mathcal{P} = \{\mathbb{P}_\theta : \theta \in \Theta\}$ be a statistical family of discrete distributions with state space $\mathcal{X}$ ...
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Interpreting base measure in exponential family as an improper prior (because entropy)

Long-time listener, first-time caller. I'm reading the Wikipedia pages on exponential families and maximum entropy probability distributions, and trying to wrap my head round the role of the base ...
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Deriving the gradient of the loss in SNE

The objective used in SNE is the KL divergence between the two distributions and is given as $$ E(Y) = \sum_i \sum_j p_{j|i}\log \frac{p_{j|i}}{q_{j|i}} $$ and the two distributions are as follows, $...
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KL divergence between function of two distributions

Suppose we have two random variables: $X \sim f(x)$ and $X' \sim f'(x)$ and we know how to compute $KL(f||f')$. We apply a one to one function $g$ on both random variables which gives us two new ...
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KL divergence in Sequential Monte Carlo

Suppose at step $t$ the particle approximation of SMC in $d$ dimensions is given by $\sum_{k=1}^N w_k\delta(\vec{x}-\vec{x}_k)$, and at the subsequent step, $t+1$ (after using Bayes' law to update ...
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Comparing approximating mixture distributions

Setup: Say I have some Bayesian predictive model I assume to be true for each observation $x_1$. Each $x_2$ is a latent/unseen/hidden random variable. The parameters are $\theta$. It's a mixture ...
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Kullback-Liebler's divergence on a conditioned function

Let $q$ be a conditioned pdf over $\mathbf{X}=X_1,\dots,X_n$ binary r.v.s in the form $$q(\mathbf{X})=\begin{cases}q_{0}(\mathbf{X}_{\setminus i}) \text{ if } X_{i}=0\\q_{1}(\mathbf{X}_{\setminus i}) \...
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Distribution with fixed mean and closest to a given distribution

I was wondering if this problem has been tackled in some way in the probability/functional analysis literature: Given a pdf $f$ such that the expectation is zero and $\mu\in\mathbb R$, find the ...
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KL-divergence as a negative log likelihood for exponential families

I am reading Distributed Estimation, Information Loss and Exponential Families, where the authors consider and compare two estimators for $\theta$ in the parametric model $p(x\mid\theta)$: the ...
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Approximation of objective based on statistical distance

I am a computer science researcher (mostly theoretical) currently in midst of statistics and not able to figure out how to proceed. At an abstract level, I have a hypothesis for an unknown ...
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270 views

Kullback Leibler divergence “efficient” upper bound

For a distribution of N values, how can I efficiently upper-bound the largest divergence between all non-negative distributions over the same random field? For example, for all distributions of a ...
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Occupancy octree metrics (Kullback-Leibler)

As I'm currently working on scan matching for outdoor environments I was wondering about the best metric to compare two occupancy octrees (one resulted from the scan matching and one ground truth ...
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Problem with Kullback–Leibler divergence criteria

I am using Kullback–Leibler divergence criteria for comparing my estimation and true density functions, but I have zero value on my estimation function when I have a testing set of size 10000, mostly ...
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Does relative Kullback-Leibler divergence exist?

Suppose I have two multivariate normal distributions. I have computed the KL divergence ($d_{KL}(N_1, N_2)$). Is there a way to measure a relative divergence between these two distributions? For ...
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Kullback-Leibler vs Hellinger Distance

I am working on this problem in which I have a dataset of $n$-dimensional examples that come from different and unknown distributions. Given a new sample, I wish to find $k$ examples from the dataset ...
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On the uniform convergence of relative frequencies of events to their probabilities

I have read the article by Vapnik, Chervonenkis "On the uniform convergence of relative frequencies of events to their probabilities" Theory of Probability and Its Applications, vol XVI, n. , 1971. ...
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Expectation of Log Likelihood Function with given parameters Proof

I have been looking for AICc value derivation employing Kullback-Leibler distance but as a result of my search I got stuck with expectation of loglikelihood. In the link loglikelihood is given as $InL(...
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How to show an alternate data processing inequality concerning KL divergence between conditionals?

Suppose $(Y,X) \sim F \in \mathcal{P(\mathbb{R^d})}$. Consider an arbitrary transformation $f$ that acts on $X$. My intuition is that the following should be a result in information theory: $$ \...
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How to use KL divergence to compare two distributions?

I am trying to model the probability distribution of a multi-dimensional dataset where all the values are discrete. Suppose the training data (represented by T) is of the shape (m, n) where n is the ...
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Markov type bound based on KL divergence?

Given two discrete distribution $p, q$ on some universe $U$, if I know they have a bounded KL divergence, say some number $c$, can I say anything about how much each point in the universe differs in ...
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A divergence that can be extended to logistic functions?

If I have data $\{(x_i, y_i)\}_{i=1}^n$ where the dependent variable is binary $(y_i = 0,1)$ I can model it using a logistic function: $$f(x; \alpha, \beta) = \frac{1}{1 + e^{-(\alpha + \beta x)}}$$ ...