itfitzme
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- Jan 29, 2012
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Here you go, the definitions of independent events and conditional probabilites.
Does that help any?
Dependent and Independent Events
The occurrence of some events may affect the probability of occurrence of others. For example, the complementary events A and A cannot occur simultaneously. If one took place the other is out of the game:
P(A|A) = 0
regardless of the probability P(A). (P(A|B) denotes the conditional probability of A assuming B.) We say that the event A is not independent of the event A (assuming P(A) ≠ 0 of couse.) And, in general, an event A is not independent of an event B iff P(A) ≠ P(A|B), i.e., if occurrence of B affects the probability of A. It may not be obvious right away, but the relationship "not independent" is symmetric: if A is not independent of B then also B is not independent of A. Formally,
if P(A) ≠ P(A|B) then P(B) ≠ P(B|A).
To see why that is so we invoke the defintion of conditional probability,
P(A|B) = P(A∩B) / P(B),
so that P(A) = P(A|B) implies P(A∩B) / P(B) = P(A), or
P(A∩B) = P(A) P(B),
which also may serve as the definition of independency of A from B. But the later relationship is symmetric! It implies
P(B) = P(A∩B)/P(A) = P(B|A),
which exactly means that B is independent of A. We see that two events A and B are either both dependent or independent one from the other. The symmetric definition of independency is this
(*)
P(A∩B) = P(A) P(B).
Two events A and B are independent iff that condition holds. They are dependent otherwise.
Dependent and Independent Events
Does that help any?