- In
mathematical statistics, the
Kullback–Leibler (KL)
divergence (also
called relative entropy and I-divergence),
denoted D KL ( P ∥ Q ) {\displaystyle...
-
Solomon Kullback (April 3, 1907 –
August 5, 1994) was an
American cryptanalyst and mathematician, who was one of the
first three employees hired by William...
- In
information theory and statistics,
Kullback's inequality is a
lower bound on the
Kullback–Leibler
divergence expressed in
terms of the
large deviations...
- |1-\alpha =e^{1-\lambda x}\}=e^{1-\lambda x}\end{aligned}}} The
directed Kullback–Leibler
divergence in nats of e λ {\displaystyle e^{\lambda }} ("approximating"...
- I(X;Y)=I(Y;X)=H(X)+H(Y)-H(X,Y).\,}
Mutual information can be
expressed as the
average Kullback–Leibler
divergence (information gain)
between the
posterior probability...
- that the
Kullback–Leibler
divergence is non-negative.
Another inequality concerning the
Kullback–Leibler
divergence is
known as
Kullback's inequality...
- the
total variation distance (or
statistical distance) in
terms of the
Kullback–Leibler divergence. The
inequality is
tight up to
constant factors. Pinsker's...
-
information radius (IRad) or
total divergence to the average. It is
based on the
Kullback–Leibler divergence, with some
notable (and useful) differences, including...
- distributions, but
these do not
include the
normal distributions as
special cases.
Kullback-Leibler
divergence (KLD) is a
method using for
compute the
divergence or...
- The
other most
important divergence is
relative entropy (also
called Kullback–Leibler divergence),
which is
central to
information theory.
There are...
