 For finite and discrete distributions, there is a straightforward definition of a concept of conditional probability. For any two events \(A\) and \(B\), the probability of \(A\) conditional on \(B\), notated as \(P(A\mid B)\), is defined as \(P(A \amp B)/P(B)\), if the probability of B is non-zero, and undefined otherwise. For any fixed \(B\), the function \(P(\_ \mid B)\) is another probability function on the same space. We can prove the Law of Total Probability. If \(B_1, B_2, B_3,\ldots\) form a partition (that is, every outcome is in exactly one of the \(B_i)\) then: