Bayes Theorem for coins problem

In summary, using Bayes Theorem, the probability of a coin being fake if it comes up heads is 2/(n+1). For part b, if a coin is flipped k times and comes up heads k times, the probability of it being fake is 2nk/n(n+1)k.
  • #1
Adyssa
203
3

Homework Statement



I have a bag of n coins, and 1 is fake - it has 2 heads.

a) Determine the probability that if I flip a coin and it comes up heads, the coin is fake.
b) If a coin is flipped k times and comes up heads k times, what is the probability that the coin is fake?

Homework Equations



Bayes Theorem: P(A|B) = [P(B|A) P(A)] / P(B)

The Attempt at a Solution



For part a) I think I have this correctly.

P(fake) = 1/n
P(!fake) = (n-1) / n
P(heads) = P(heads|fake) P(fake) + P(heads|!fake) P(!fake)
= 1 * 1/n + 1/2 * (n-1) / n
= 1/n + (n-1) / 2n
= 2/2n + (n-1) / 2n
= (n+1) / 2n

P(fake|heads) = [P(heads|fake) P(fake)] / P(heads)
= 1 * 1/n / (n+1) / 2n
= 1/n / (n+1) / 2n
= 1/n * 2n / (n+1)
= 2n / n(n+1)
= 2 / (n+1)

For part b), I'm a little stuck. I think the probability of the coin coming up heads k times = P(heads)k or [(n+1) / 2n]k. My reasoning here is that ignoring the fake coin the probability of getting 5 heads in a row would be 1/2 * 1/2 * 1/2 * 1/2 * 1/2 = (1/2)k.

So P(fake|(k heads)) = [P((k heads) | fake) * P(fake)] / P(k heads)
and P((k heads) | fake) = 1, so

P(fake|(k heads)) = [1 * 1/n] / [(n+1) / 2n]k
= [1/n] / [(n+1) / 2n]k
= 1/n * [2n / (n+1)]k
= 1/n * [2nk/(n+1)k]
= 2nk / n(n+1)k

But I'm unsure of my reasoning.
 
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  • #2
I think the point is that you flip the same coin k times and it comes up heads k times. So the probability of getting that from a real coin is P(k heads|!fake) = 1/2^k, and P(k heads|fake)=1 . Otherwise the calculation is exactly the same.
 
Last edited:

1. What is Bayes Theorem for coins problem?

Bayes Theorem for coins problem is a statistical method used to determine the probability of an event occurring based on prior knowledge of related events. It is commonly used in situations where there are multiple possible outcomes, such as flipping a coin.

2. How does Bayes Theorem for coins problem work?

Bayes Theorem for coins problem uses the formula P(A|B) = (P(B|A) * P(A)) / P(B) to calculate the probability of event A occurring given that event B has already occurred. This takes into account prior knowledge and new evidence to update the probability of an event.

3. What is the difference between prior probability and posterior probability in Bayes Theorem for coins problem?

Prior probability refers to the initial probability of an event occurring before any new evidence is taken into account. Posterior probability, on the other hand, is the updated probability of an event occurring after new evidence has been considered using Bayes Theorem.

4. How is Bayes Theorem for coins problem used in real-world situations?

Bayes Theorem for coins problem can be used in various real-world situations, such as predicting the occurrence of a disease based on symptoms, determining the likelihood of a stock market trend based on historical data, or estimating the chance of rain based on current weather conditions and past weather patterns.

5. What are the limitations of Bayes Theorem for coins problem?

Bayes Theorem for coins problem relies on the assumption that prior probabilities and new evidence are independent of each other. In real-world situations, this may not always be the case, leading to inaccurate results. Additionally, the accuracy of the results is highly dependent on the quality and reliability of the initial data and prior knowledge used.

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