Find conditional probability mass function

In summary, finding the conditional probability mass function involves determining the probability of a discrete random variable given the occurrence of another event. This is done by using the formula \( P(X|Y) = \frac{P(X \cap Y)}{P(Y)} \), where \( P(X|Y) \) is the conditional probability of \( X \) given \( Y \), \( P(X \cap Y) \) is the joint probability of both events occurring, and \( P(Y) \) is the probability of event \( Y \). This function helps in understanding the relationship between variables and making predictions based on known conditions.
  • #1
psie
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Homework Statement
Consider a fair die thrown twice. Let ##U_1## be the dots on the first throw and ##U_2## on the second (intuitively, they are independent). Let ##X=U_1+U_2## and ##Y=\min(U_1,U_2)##. Find the conditional probability (mass) function ##p_{Y|X=x}(y)## and the conditional distribution ##F_{Y|X=x}(y)##.
Relevant Equations
We have $$p_{Y|X=x}(y)=\frac{p_{X,Y}(x,y)}{p_X(x)}=\frac{p_{X,Y}(x,y)}{\sum_z p_{X,Y}(x,z)},$$and $$F_{Y|X=x}(y)=\frac{\sum_{z\leq y}p_{X,Y}(x,z)}{p_X(x)}=\frac{\sum_{z\leq y} p_{X,Y}(x,z)}{\sum_z p_{X,Y}(x,z)}.$$
I don't really know how to approach this problem, but my plan is to find ##p_{X,Y}(x,y)=P(X=x,Y=y)##. The two conditions ##X=x## and ##Y=y## in terms of ##U_1## and ##U_2## read (I think) $$U_1=y,U_2 = x-y \text{ or }U_2 = y, U_1 = x-y,\qquad x\geq 2y.$$ So $$P(X = x, Y = y) = \begin{cases} P(U_1 = y, U_2 = x-y\text{ or }U_2 = y, U_1 = x-y) & x\geq 2y \\ 0 & x < 2y\end{cases}.$$ I don't know how to proceed and find ##p_{Y|X=x}(y)##.
 
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  • #2
I think you are severely overcomplicating things. Both ##X## and ##Y## are functions of ##U_1## and ##U_2## whose probability distributions are very simple. For a given ##X = x##, what are the possible values of ##U_1## and ##U_2##? What are the probabilities of each combination? What does this mean for ##Y##?
 
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  • #3
Orodruin said:
I think you are severely overcomplicating things. Both ##X## and ##Y## are functions of ##U_1## and ##U_2## whose probability distributions are very simple. For a given ##X = x##, what are the possible values of ##U_1## and ##U_2##? What are the probabilities of each combination? What does this mean for ##Y##?
I appreciate the reply, but I feel very hopeless about this. If we fix an ##x##, then the possible values of ##U_1,U_2## are ##U_1=y,U_2=x-y## or ##U_2=y,U_2=x-y##, and we want ##x\geq 2y##. But then I'm just getting back into what I did above. Could you elaborate on your approach a bit more? Maybe show how you would obtain the conditional pmf, and from there I can see if I can either modify my approach or go with yours.
 
  • #4
psie said:
Could you elaborate on your approach a bit more?

Please try to answer the questions I posed in the previous post. You only answered the first:
Orodruin said:
For a given ##X = x##, what are the possible values of ##U_1## and ##U_2##?

The second and third are very significant:
Orodruin said:
What are the probabilities of each combination? What does this mean for ##Y##?
Edit: Note: I am talking about the probabilities conditioned on ##X = x## here. It is the only thing you need to look at.
 
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  • #5
Here is an illustration to help your thought process:
1718746744453.png

If the general case is too hard to start with, focus on this special case of x = 9. What are the conditional distributions of ##U_1## and ##U_2##. Can you fill out the values of ##Y## in this square? What is the conditional probability to find ##Y = 4##?
 
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  • #6
psie said:
If we fix an ##x##, then the possible values of ##U_1,U_2## are ##U_1=y,U_2=x-y## or ##U_2=y,U_2=x-y##, and we want ##x\geq 2y##.

Ok, i just reread this and realized it actually did not really answer my first question either. The first question asks what is the possible combinations of ##U_1## and ##U_2## given ##X = x##. The value of ##Y## does not enter into the question, just the conditioning on ##X##.
 
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  • #7
Orodruin said:
Here is an illustration to help your thought process:
View attachment 347103
If the general case is too hard to start with, focus on this special case of x = 9. What are the conditional distributions of ##U_1## and ##U_2##. Can you fill out the values of ##Y## in this square? What is the conditional probability to find ##Y = 4##?
Ok, thank you for elaborating. I don't see the general case right now. In this special case, the conditional pmf of ##U_1## and ##U_2## are, I believe, ##\frac14## (since ##3,4,5## and ##6## have equal probability of appearing). The values of ##Y## are ##3## or ##4##, also with equal probability, so the conditional probability must be ##\frac12## to find ##Y=4## given ##X=9##. Is this correct?
 
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  • #8
psie said:
the conditional probability must be 1/2 to find Y=4 given X=9. Is this correct?
Yes. Even values of X are a little trickier.
 
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  • #9
haruspex said:
Yes. Even values of X are a little trickier.
Although still not very tricky …
 

FAQ: Find conditional probability mass function

What is a conditional probability mass function?

A conditional probability mass function (PMF) describes the probability of a discrete random variable given that another random variable takes on a specific value. It is denoted as P(X|Y) and provides insight into how the distribution of one variable changes in the context of another variable.

How do you calculate the conditional PMF?

The conditional PMF can be calculated using the formula P(X=x|Y=y) = P(X=x, Y=y) / P(Y=y), where P(X=x, Y=y) is the joint PMF of X and Y, and P(Y=y) is the marginal PMF of Y. This formula applies as long as P(Y=y) is greater than zero.

What is the relationship between joint PMF and conditional PMF?

The joint PMF P(X=x, Y=y) can be expressed in terms of the conditional PMF and the marginal PMF. Specifically, P(X=x, Y=y) = P(X=x|Y=y) * P(Y=y). This relationship allows us to derive one from the other, provided we have the necessary information.

Can conditional PMF be greater than 1?

No, the conditional PMF cannot be greater than 1. Since it represents a probability, the values of a conditional PMF must lie between 0 and 1, inclusive. If the computed value exceeds 1, it indicates an error in the calculations or assumptions made.

What are some applications of conditional PMF?

Conditional PMFs are widely used in various fields such as statistics, machine learning, and decision-making. They help in understanding relationships between variables, making predictions based on prior knowledge, and analyzing the effects of one variable on another in experiments and real-world scenarios.

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