For a Gaussian random variable of unitary variance, the entropy is given by
$H = 0.5\log(2 \pi e)$. Also, the mutual information (MI) between correlated Gaussian random variables is given by $MI = -0.5\log(1 – \rho^2)$, where $\rho$ denotes correlation. With these equations in mind, I can think of values for $\rho$ with which the MI is larger than the joint entropy between the two random variables. Am I missing something here or the MI may indeed be larger than the joint entropy?
Can the mutual information be larger than the joint entropy
entropyinformation theory
Best Answer
Not quite. That's the differential entropy, which is a very different thing from the Shannon entropy. You cannot compare in a meaningful mutual information with differential entropies.
In particular, calling $h()$ the differential entropies, it's true that
$$I(X;Y) = h(X) - h(X|Y)$$
but it's not true that $h(X|Y) \ge 0$. Hence, the mutual information can be larger that the (differential) entropy of any variable (or the joint two variables).
See also here and here and here...