The Ultimate Law of total probability Dog Breeds Information Guide and Reference

Nomenclature in probability theory is not wholly standard.

Sometimes the phrase law of total probability refers to the proposition that if { B_n : n = 1, 2, 3, ... } is a finite or countably infinite partition of a probability space and each set B_n is measurable, then for any event A we have

$P(A)=\sum_{n} P(A\cap B_n).$

This is also sometimes called the law of alternatives.

The phrase law of total probability is also used to refer to the proposition that says that under similar assumptions, we have

$P(A)=\sum_{n} P(A\mid B_n)P(B_n),$

which may be rephrased as

$P(A)=E(P(A\mid N))$

where N is a random variable equal to n with probability P(B_n). It may be stated even more efficiently thus:

The prior probability of A is equal to the prior expected value of the posterior probability of A.

Law of total probability

See also