CE | R Documentation |
H(X | Y)
Compute Shannon's Conditional-Entropy based on the chain rule H(X | Y)
= H(X,Y) - H(Y)
based on a given joint-probability vector P(X,Y)
and
probability vector P(Y)
.
CE(xy, y, unit = "log2")
xy |
a numeric joint-probability vector |
y |
a numeric probability vector |
unit |
a character string specifying the logarithm unit that shall be used to compute distances that depend on log computations. |
This function might be useful to fastly compute Shannon's Conditional-Entropy for any given joint-probability vector and probability vector.
Shannon's Conditional-Entropy in bit.
Note that the probability vector P(Y) must be the probability
distribution of random variable Y ( P(Y) for which H(Y) is computed ) and
furthermore used for the chain rule computation of H(X | Y) = H(X,Y) -
H(Y)
.
Hajk-Georg Drost
Shannon, Claude E. 1948. "A Mathematical Theory of Communication". Bell System Technical Journal 27 (3): 379-423.
H
, JE
CE(1:10/sum(1:10),1:10/sum(1:10))
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