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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 probability \(\frac{1}{2}\) Find the conditional probability that component 1 works given that the system is functioning.

Short Answer

Expert verified
The conditional probability that component 1 works given that the system is functioning is \(P(A|B) = \frac{1}{2 - \left(\frac{1}{2}\right)^{n-1}}\).

Step by step solution

01

Probability of the system functioning

To calculate the probability of the entire system functioning, we need to find the complement - the probability that none of the components are working. Since each component works with probability \(\frac{1}{2}\), it also fails with probability \(\frac{1}{2}\). The probability of all components failing, \(P\left( \bigcap_{i=1}^n \text{component }i\text{ fails} \right)\), considering that they work independently: \(P\left( \bigcap_{i=1}^n \text{component }i\text{ fails} \right) = \left(\frac{1}{2}\right)^n\) Now, we can find the probability of the entire system functioning, which is the complement of the probability of all components failing: \(P(B) = 1 - P\left( \bigcap_{i=1}^n \text{component }i\text{ fails} \right) = 1 - \left(\frac{1}{2}\right)^n\) #Step 2: Find the probability of component 1 working and the system functioning#
02

Probability of component 1 working and system functioning

In this step, we need to calculate the probability of component 1 working, given that the system is functioning. We'll first consider the probability both component 1 is working and the system is functioning: \(P(A \cap B) = P(\text{component 1 works} \cap \text{system functions})\) Since the system is functioning if and only if at least one component is working, the system is also functioning if component 1 is working. Therefore, we know: \(P(A \cap B) = P (\text{component 1 works}) = \frac{1}{2}\) #Step 3: Calculate the conditional probability# Now we can apply the definition of conditional probability, \(P(A|B) = \frac{P(A \cap B)}{P(B)}\):
03

Conditional probability of component 1 working given system functioning

\(P(A|B) = \frac{P(\text{component 1 works} \cap \text{system functions})}{P(\text{system functions})} = \frac{\frac{1}{2}}{1 - \left(\frac{1}{2}\right)^n} = \frac{1}{2 - \left(\frac{1}{2}\right)^{n-1}}\) Thus, the conditional probability that component 1 works given that the system is functioning is \(\frac{1}{2 - \left(\frac{1}{2}\right)^{n-1}}\).

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Parallel System Reliability
A parallel system is structured so that it continues to function as long as at least one of its components works. This is a significant advantage because it adds reliability to the overall system. If one component fails, the others can continue to operate the system, reducing potential downtime.
This kind of system is characterized by redundancy and is beneficial in critical applications where continuous operation is essential, such as in electrical grids or computing networks.
Independent Events
Independent events are scenarios in which the occurrence of one event does not influence the occurrence of another. In the context of our parallel system, each component's performance is independent of the others.
This independence implies that the failure or success of one component does not impact the probability of another component working or failing. Understanding this independence is crucial, especially when calculating probabilities in systems involving multiple components.
Component Failure Probability
Component failure probability refers to the likelihood that an individual component in the system will fail. In our exercise, each component has a probability of failure, which is \( 1 - P(\text{component works}) = \frac{1}{2} \).
  • This implies that each component is equally likely to fail or work.
  • The independence and equal probability allow us to calculate the overall reliability of the system using combinatorial probabilities.
Complement Rule
The complement rule is a probability theory concept used to find the probability of the complementary event. It states that the probability of the occurrence of an event and its complement equals 1.
This rule is extremely useful when dealing with parallel systems. In our exercise, we used the complement rule to find the probability of the system functioning by calculating the probability of no component working, and then subtracting it from 1.
  • This technique simplifies calculations, especially when directly calculating the functioning probability of complex systems is more challenging.

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