Conditional Probability Help -- Is this a trick question?

In summary, based on Bayes' theorem, the probability that Tom actually rode on the fighter jet increases as his certainty of actively recalling the memory decreases. However, even with 25% certainty, the probability is still very low at 0.4%. This problem highlights the importance of considering prior odds and adjusting them with new information using Bayes' theorem.
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redsox123
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A conditional probability problem that I can't figure out: (PART 1) Tom is trying to figure out the probability that his memory of a ride in a fighter jet when he was a toddler is real or if it is just a product of his imagination. He grew up near an air force base -- the odds of a child in that area taking a ride on a fighter jet are 1/1000. Because he has no bias towards whether the event happened or not -- he is 50 percent sure it did, 50 percent sure it did not -- per Bayes's theory, there is a 1/1000 chance it did, the prior odds. (Is the assumption above true (y/n): Y(PART 2) If it is true, please answer the following: Of those that correctly recalled riding on the fighter jet (they actually did), 1/2 remembered spontaneously and 1/2 were actively thinking about their childhood. Of those that did not ride on the jet and merely imagined it, 1/100 remembered spontaneously, 99/100 were actively thinking about their childhood. If we were 100 percent sure that Tom remembered while actively thinking about his childhood, how does this affect the probability that he actually rode on the fighter jet? What if we were 75 percent certain? And 50 percent? And, finally, 25 percent? Is it impossible to calculate? To start, we can conclude that of 1000 children the following is true: RODE JET / REMEMBERED (1 child)......DIDN'T RIDE JET / IMAGINED (999 children)
a. Spontaneous Recall (1/2 * 1) = 1/2 child ...c. Spontaneous Recall (1/100 * 999) = 9.99 children
b. Active Recall (1/2 * 1) = 1/2 child..... d. Active Recall (99/100 * 999) = 989.01 children

Regarding 100 percent -- this seems straight-forward. If Tom is 100 percent sure he remembered while actively thinking about his childhood, then the odds of him riding on the jet would be expressed as b / d.And the probability would be unchanged for 50 percent -- this assumes we have no idea if the memory was spontaneously recalled or not, so the prior odds would remain. Regarding 25 and 75 percent: the only way I can think of solving is by averaging the values we got for 100 and 50 percent. What would be the formulaic way to solve? I thought I was pretty proficient at Bayes' theorem, I just can't wrap my head around this one. I don't know which prior probability I should start with.
 
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To solve this problem using Bayes' theorem, we need to start with the prior odds and then adjust them based on the additional information. For 100% certainty:P(Rode Jet | Active Recall) = P(Active Recall | Rode Jet) * P(Rode Jet) / P(Active Recall) P(Rode Jet | Active Recall) = (1/2 * 1/1000) / (1/2 * 1 + 99/100 * 999)= 0.001 / 0.9995= 0.1%For 75% certainty:P(Rode Jet | Active Recall) = P(Active Recall | Rode Jet) * P(Rode Jet) / ((0.75 * P(Active Recall|Rode Jet)) + (0.25 * P(Active Recall|Did Not Ride Jet)))P(Rode Jet | Active Recall) = (1/2 * 1/1000) / ((0.75 * 1/2) + (0.25 * 99/100))= 0.001 / 0.5025= 2%For 50% certainty:P(Rode Jet | Active Recall) = P(Active Recall | Rode Jet) * P(Rode Jet) / ((0.5 * P(Active Recall|Rode Jet)) + (0.5 * P(Active Recall|Did Not Ride Jet)))P(Rode Jet | Active Recall) = (1/2 * 1/1000) / ((0.5 * 1/2) + (0.5 * 99/100))= 0.001 / 0.495= 0.2%For 25% certainty:P(Rode Jet | Active Recall) = P(Active Recall | Rode Jet) * P(Rode Jet) / ((0.25 * P(Active Recall|Rode Jet)) + (0.75 * P(Active Recall|Did Not Ride Jet)))P(Rode Jet | Active Recall) = (1/2 * 1/1000) / ((0.25 * 1/2) + (0.75 * 99/100))= 0.001 / 0.24875= 0.4%
 

1. What is conditional probability?

Conditional probability is the measure of the likelihood of an event occurring given that another event has already occurred. It is calculated by dividing the probability of the intersection of the two events by the probability of the first event.

2. How is conditional probability different from regular probability?

Regular probability considers the likelihood of an event occurring without any additional information, while conditional probability takes into account the occurrence of another event.

3. Can you give an example of conditional probability?

One example of conditional probability is the chance of rolling a 6 on a fair die if it is already known that the number rolled is an even number. The probability of rolling a 6 in this case would be 1/3, as there are only 3 even numbers on a die (2, 4, and 6) and only one of them is a 6.

4. What is the formula for calculating conditional probability?

The formula for conditional probability is P(A|B) = P(A ∩ B) / P(B), where P(A|B) is the conditional probability of event A given event B has occurred, P(A ∩ B) is the probability of the intersection of events A and B, and P(B) is the probability of event B.

5. How is conditional probability used in real life?

Conditional probability is used in a variety of fields, such as economics, medicine, and psychology, to make predictions and decisions based on available information. For example, in medicine, doctors may use conditional probability to determine the likelihood of a patient having a certain disease based on their symptoms and medical history.

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