CAF123 said:
Homework Statement
A parallel system functions whenever at least one of its components works. Consider a parallel system of ##n## components, and suppose that each component works independently with prob 1/2. FInd the conditional prob that component 1 works given that the system is functioning.
The Attempt at a Solution
Let E be the event that compt 1 works and F the event that the system functions.
P(E|F) = P(EF)/P(F) = P(F|E)P(E)/P(F). I have the numerator correct, I just have a subtle question about the denominator.
When I see the words 'at least', I always jump to the conclusion of doing 1-P(less than 'at least'). Is this a normal practice?
So I get the correct answer for the denominator using this way. However, I also considered finding the denominator by conditioning on the component that works. So I said;
Let ##G_i## be the event that compt i works. Then $$P(F) = P(\cup_{i=1}^{n} F|G_i) = P(F|G_1)P(G_1) + P(F|G_2)P(G_2) +...+ P(F|G_n)P(G_n)$$. Correct me if I am wrong, but I believe this method fails because this assumes that the system functions when only one component works? In general, a union is where we want only one event to happen out of,say, n possibilities? Thanks!
If E occurs then F occurs (since at least component 1 is functioning); that is, E is a subset of F, so EF = E, giving P(E|F) = P(E)/P(F). Of course, P(F) = 1 - P{all broken}, just as you said; and yes, that is often the best way to do such computations and is a completely normal practice.
To get the denominator the hard way, you need to compute
[tex]P(F) = P\{\cup_{i=1}^n G_i \}.[/tex] For example, if n = 3 this would be given by
[tex]P(F) = P(G_1) + P(G_2) - P(G_1 G_2) = 1/2 + 1/2 - 1/4 = 3/4.[/tex] For larger values of n this gets more complicated, but you can appeal to general versions of the inclusion/exclusion principle as found, eg., in Feller, "An Introduction to Probability Theory and its Applications", Vol. I, Wiley (1968)----my very favorite probability book---and apply the formulas in Chapter IV. Specifically, if
[tex]S_1 = \sum_i P(G_i), \; S_2 = \sum_{i,j:i < j}P(G_i G_j), \; <br />
S_3 = \sum_{i,j,k: i < j < k} P(G_i G_j G_k),\\<br />
\;\;\;\;\; \vdots\\<br />
S_n = P(G_1 G_2 \cdots G_n),[/tex]
then the probability that at least r of the events G_i occur is
[tex]P_r = S_r - {r \choose r-1} S_{r+1} + {r+1 \choose r-1} S_{r+2}<br />
- {r+2 \choose r-1} S_{r+3} + \cdots \pm {n-1 \choose r-1} S_n.[/tex]
In your case you can simplify this, using the fact that
[tex]S_m = {n \choose m} 1/2^m,[/tex] because S_m is just the number of m-tuples chosen from 1,2,...,n, multiplied by a probability that is the same for any m-tuple. It simplifies even more because you only need the case r = 1, so
[tex]\text{answer } = S_1 - S_2 + S_3 - \cdots \pm S_n,[/tex] and if you write this out in more detail something very nice happens.