Intuitively, mutual information measures the information that formula_2 and formula_3 share: It measures how much knowing one of these variables reduces uncertainty about the other. For example, if formula_2 and formula_3 are independent, then knowing formula_2 does not give any information about formula_3 and vice versa, so their mutual information is zero. At the other extreme, if formula_2 is a deterministic function of formula_3 and formula_3 is a deterministic function of formula_2 then all information conveyed by formula_2 is shared with formula_3: knowing formula_2 determines the value of formula_3 and vice versa. As a result, in this case the mutual information is the same as the uncertainty contained in formula_3 (or formula_2) alone, namely the entropy of formula_3 (or formula_2). Moreover, this mutual information is the same as the entropy of formula_2 and as the entropy of formula_3. (A very special case of this is when formula_2 and formula_3 are the same random variable.)