Ann: Pause in Trading, page-278

The law of total probability is^[1] the proposition that if {\displaystyle \left\{{B_{n}:n=1,2,3,\ldots }\right\}} is a finite or countably infinite partition of a sample space (in other words, a set of pairwise disjoint events whose union is the entire sample space) and each event {\displaystyle B_{n}} is measurable, then for any event {\displaystyle A} of the same probability space:
{\displaystyle \Pr(A)=\sum _{n}\Pr(A\cap B_{n})}
or, alternatively,^[1]
{\displaystyle \Pr(A)=\sum _{n}\Pr(A\mid B_{n})\Pr(B_{n}),}
where, for any {\displaystyle n} for which {\displaystyle \Pr(B_{n})=0} these terms are simply omitted from the summation, because {\displaystyle \Pr(A\mid B_{n})} is finite.
The summation can be interpreted as a weighted average, and consequently the marginal probability, {\displaystyle \Pr(A)}, is sometimes called "average probability";^[2] "overall probability" is sometimes used in less formal writings.^[3]
The law of total probability can also be stated for conditional probabilities. Taking the {\displaystyle B_{n}} as above, and assuming {\displaystyle C} is an event independent with any of the {\displaystyle B_{n}}:
{\displaystyle \Pr(A\mid C)=\sum _{n}\Pr(A\mid C\cap B_{n})\Pr(B_{n}\mid C)=\sum _{n}\Pr(A\mid C\cap B_{n})\Pr(B_{n})}




Well, check the math, throw out enough figures plucked from your.... crystal ball.... and you were bound to be right eventually mate! Good work though, what's your next prediction? It was 15, now 14 - then lemme guess, 13?