Are the events A1 and A2 conditionally independent, given F2?

In summary, the conversation discusses the concept of conditional independence in relation to Mendel's pea experiment. The main question is whether events A1 and A2, representing flower color in two F3 seeds, are conditionally independent given F2. The conversation also mentions the probabilities for each event, and suggests using exhaustion to calculate the final probability.
  • #1
dspampi
16
0
I was able to do the first part of this problem but unsure how to approach this:

Mendel, revisited: Mendel’s peas had either purple or white flowers; flower color is due to a single gene, for which the purple allele (A) is dominant to the white allele (a). We cross two pure-breeding lines (one purple and one white) to produce the F1 hybrid. We self the F1 and choose an F2 seed at random. We grow and self the F2 and choose two F3 seeds at random. Consider the following events A1 = {F3 number 1 has purple flowers} and A2 = {F3 number 2 has purple flowers}.

(b) Are A1 and A2 conditionally independent, given F2? In other words, is Pr(A1 and A2| F2) = Pr(A1|F2) × Pr(A2| F2)?

Say for instance if F2 where heterozygote (Aa); ...AA means homozygote (purple) and aa (white)

I know:

P(A1 and A2) = P(A1 and A2 | Aa) P(Aa) + P(A1 and A2 | AA) P(AA) + P(A1 and A2 | aa) P(aa)
=(3/4)^2*(1/2)+1*(1/4)+0*(1/2)= 0.53
P(A1) = P(A2) = P(A1 | Aa) P(Aa) + P(A1 | AA) P(AA) + P(A1 | aa) P(aa)
= (3/4)*(1/2) + 1*(1/4) + 0*(1/4) = 0.625
 
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  • #2
Hey dspampi and welcome to the forums.

Have you tried instead of first calculating P([A1 AND A2]|F2) and then calculating separately P(A1|F2) and P(A2|F2)?

So for P([A1 AND A2]|F2) = P(A1 AND A2 AND F2)/P(F2) and P(A1|F2) = P(A1 AND F2)/P(F2) and P(A2|F2) = P(A2 AND F2)/P(F2).

I don't know anything about your biological probabilities, but you may have to do it by exhaustion. In other words do it for all possible values of F2 if you have some non-general distribution (You basically evaluate all of the probabilities for all possibilities of the random variable F2).

It would help if you posted what your probabilities are for each event for your random variables.

It looks like you have done the exhaustion for P(A1 and A2) but you haven't included the exhaustion for the F2 random variable. Doing this and combining it with your other results should give you the answer you need.
 

What is conditional probability?

Conditional probability is a measure of the likelihood of an event occurring, given that another event has already occurred. It is expressed as P(A|B), where A is the event of interest and B is the event that has already occurred.

How is conditional probability calculated?

Conditional probability is calculated by dividing the probability of the joint occurrence of both events (P(A and B)) by the probability of the event that has already occurred (P(B)). In mathematical notation, it is expressed as P(A|B) = P(A and B) / P(B).

Can conditional probability be greater than 1?

No, conditional probability cannot be greater than 1. This is because the probability of an event occurring cannot be higher than the probability of both events occurring together.

How is conditional probability used in real life?

Conditional probability is used in various fields such as statistics, machine learning, and genetics. In real life, it can be used to predict the likelihood of a disease based on a person's symptoms, or to determine the probability of a customer making a purchase based on their previous buying behavior.

What is the difference between conditional probability and joint probability?

Conditional probability refers to the likelihood of an event occurring given that another event has already occurred, while joint probability refers to the likelihood of two events occurring together. Conditional probability is calculated using joint probability, but they are not the same concept.

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