In mathematical statistics, the Kullback–Leibler (KL) divergence (also called relative entropy and I-divergence), denoted D KL ( P ∥ Q ) {\displaystyle...

as information radius (IRad) or total divergence to the average. It is based on the Kullback–Leibler divergence, with some notable (and useful) differences...

called Kullback–Leibler divergence), which is central to information theory. There are numerous other specific divergences and classes of divergences, notably...

George Washington University. The Kullback–Leibler divergence is named after Kullback and Richard Leibler. Kullback was born to Jewish parents in Brooklyn...

The only divergence on Γ n {\displaystyle \Gamma _{n}} that is both a Bregman divergence and an f-divergence is the Kullback–Leibler divergence. If n ≥...

of ln(xk) equal to ln(λk) −  γ {\displaystyle \gamma } . The Kullback–Leibler divergence between two Weibulll distributions is given by D KL ( W e i b...

at the National Security Agency, he and Solomon Kullback formulated the Kullback–Leibler divergence, a measure of similarity between probability distributions...

Q_{\hat {\theta }}} ) that has a minimal distance, in terms of Kullback–Leibler divergence, to the real probability distribution from which our data were...

dimensionality of the vector space, and the result has units of nats. The Kullback–Leibler divergence from N 1 ( μ 1 , Σ 1 ) {\displaystyle {\mathcal {N}}_{1}({\boldsymbol...

defined a spectrum of divergence measures generalising the Kullback–Leibler divergence. The Rényi divergence of order α or alpha-divergence of a distribution...

(X)=k+\ln \theta +\ln \Gamma (k)+(1-k)\psi (k).} The Kullback–Leibler divergence (KL-divergence), of Gamma(αp, βp) ("true" distribution) from Gamma(αq...

P_{Y})} where D K L {\displaystyle D_{\mathrm {KL} }} is the Kullback–Leibler divergence, and P X ⊗ P Y {\displaystyle P_{X}\otimes P_{Y}} is the outer...

normal distributions as special cases. Kullback-Leibler divergence (KLD) is a method using for compute the divergence or similarity between two probability...

=e^{1-\lambda x}\}=e^{1-\lambda x}\end{aligned}}} The directed Kullback–Leibler divergence in nats of e λ {\displaystyle e^{\lambda }} ("approximating"...

the Kullback-Leibler divergence between Cauchy distributions". arXiv:1905.10965 [cs.IT]. Nielsen, Frank; Okamura, Kazuki (2023). "On f-Divergences Between...

of the relative entropy (i.e., the Kullback–Leibler divergence); specifically, it is the Hessian of the divergence. Alternately, it can be understood...

In probability theory and statistics, the normal-inverse-gamma distribution (or Gaussian-inverse-gamma distribution) is a four-parameter family of multivariate...

fit to the distribution) because the ELBO includes a Kullback-Leibler divergence (KL divergence) term which decreases the ELBO due to an internal part...

has a well-specified asymptotic distribution. The Kullback–Leibler divergence (or information divergence, information gain, or relative entropy) is a way...

} The G-test statistic is proportional to the Kullback–Leibler divergence of the theoretical distribution from the empirical distribution:...

Divergence (statistics), a measure of dissimilarity between probability measures Bregman divergence f-divergence Jensen–Shannon divergence Kullback–Leibler...

functions of two generalized gamma distributions, then their Kullback-Leibler divergence is given by D K L ( f 1 ∥ f 2 ) = ∫ 0 ∞ f 1 ( x ; a 1 , d 1 ...

be formulated using the Kullback–Leibler divergence D K L ( p ∥ q ) {\displaystyle D_{\mathrm {KL} }(p\parallel q)} , divergence of p {\displaystyle p}...

density functions of two Gompertz distributions, then their Kullback-Leibler divergence is given by D K L ( f 1 ∥ f 2 ) = ∫ 0 ∞ f 1 ( x ; b 1 , η 1 )...

points in the low-dimensional map, and it minimizes the Kullback–Leibler divergence (KL divergence) between the two distributions with respect to the locations...

(X|Y)=\mathrm {H} (X,Y)-\mathrm {H} (Y).\,} The Kullback–Leibler divergence (or information divergence, information gain, or relative entropy) is a way...

variation distance (or statistical distance) in terms of the Kullback–Leibler divergence. The inequality is tight up to constant factors. Pinsker's inequality...

theory and machine learning, information gain is a synonym for Kullback–Leibler divergence; the amount of information gained about a random variable or...

that the Kullback–Leibler divergence is non-negative. Another inequality concerning the Kullback–Leibler divergence is known as Kullback's inequality...

information is related to relative entropy. The relative entropy, or Kullback–Leibler divergence, between two distributions p {\displaystyle p} and q {\displaystyle...