The joint entropy of two discrete random variables and is defined as the entropy of the joint distribution of and :
If and are independent, then the joint entropy is simply the sum of their individual entropies.
(Note: The joint entropy should not be confused with the cross entropy, despite similar notations.)
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Famous quotes containing the words joint and/or entropy:
“There is no such thing as “the Queen’s English.” The property has gone into the hands of a joint stock company and we own the bulk of the shares!”
—Mark Twain [Samuel Langhorne Clemens] (1835–1910)
“Just as the constant increase of entropy is the basic law of the universe, so it is the basic law of life to be ever more highly structured and to struggle against entropy.”
—Václav Havel (b. 1936)