Description Usage Arguments Details Value See Also
Compute the KullbackLeibler divergence of one probability distribution from another. This divergence is also known as the relative entropy, the information deviation, and the information gain.
1 2  ## S4 method for signature 'Distribution,Distribution'
KullbackLeibler(p1, p2)

p1, p2 

Let p1
and p2
denote the vectors of probability mass assigned
by two distributions defined on the same state space; furthermore, let
these distributions be strictly positivelyvalued. Then, the KullbackLeibler
divergence of p2
from p1
is given by sum(p1 * log(p1 / p2))
.
Note that the terminology "divergence of p2
from p1
" indicates
that p1
is the reference distribution against which the distribution
p2
is evaluated.
KullbackLeibler divergence is not a symmetric function. That is, it is not
generally true that KullbackLeibler(p1, p2) = KullbackLeibler(p2, p1)
.
Symmetric functions based on the KullbackLeibler divergence are available
through the Jeffrey and Topsoe distance.
The KullbackLeibler divergence of p2
from p1
.
JensenShannon
, Jeffrey
, Topsoe
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