Is X1 Given S=s a Binomial Distribution in Poisson Variables?

In summary, the question is whether X1 given S=s is a binomial distribution with unknown parameters. While S is shown to be a Poisson distribution with mean μ1 + μ2, it cannot be confirmed if X1 given S is binomial or not. One person suggests that it could be binomial, but another points out that S cannot be binomial due to the range of values for X2. It is then clarified that the question is about the conditional distribution of X1 when S is a fixed value, but it is noted that in this setting X1 cannot range over all integer values. The conversation ends with a formula showing the potential binomial distribution of X1 given S=s.
  • #1
johnnytzf
3
0
let X1 and X2 be independent Poisson variables with respective parameters μ1 and μ2. Let S = X1 + X2. Is X1 given S=s a binomial dsitribution? What is the parameters?


I just can show that S is a Poisson with mean μ1 + μ2. But I am not confirm X1 given S is a binomial or not? Someone please help to prove it.
 
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  • #2
S cannot be binomial. X2 ranges over all integers. Therefore S will also, while binomial range is finite.
 
  • #3
If the conditional distribution is intended you want to know about X1 when S = X1 + X2 = s, a fixed value. In this setting X1 cannot range over all integer values.
 
  • #4
statdad said:
If the conditional distribution is intended you want to know about X1 when S = X1 + X2 = s, a fixed value. In this setting X1 cannot range over all integer values.

You're right. I misread the question.

It looks binomial:

P(X1=x|S=s) = (μ1xμ2(s-x))/{x!(s-x)!(μ1+μ2)s}
 
Last edited:
  • #5
mathman said:
You're right. I misread the question.

It looks binomial:

P(X1=x|S=s) = (μ1xμ2(s-x))/{x!(s-x)!(μ1+μ2)s}

I was sloppy. There should be a coefficient of s!
 

Related to Is X1 Given S=s a Binomial Distribution in Poisson Variables?

1. What is a conditional distribution?

A conditional distribution is a probability distribution that shows the likelihood of an event occurring, given that another event has already occurred. It is used to model relationships between two variables and can help us understand how one variable affects the probability of another variable.

2. How is a conditional distribution different from a joint distribution?

A joint distribution shows the probability of two events occurring simultaneously, while a conditional distribution shows the probability of one event occurring given that another event has already occurred. In other words, a joint distribution considers all possible outcomes, while a conditional distribution focuses on a subset of those outcomes.

3. How do you calculate a conditional distribution?

To calculate a conditional distribution, you first need to have a joint distribution. Then, you can use the formula P(A|B) = P(A and B) / P(B), where P(A|B) represents the conditional probability of event A given event B has occurred, P(A and B) represents the joint probability of both events occurring, and P(B) represents the probability of event B occurring.

4. What is the importance of conditional distribution in statistics?

Conditional distribution allows us to understand the relationship between two variables and how one variable affects the probability of another. This information is crucial in making predictions and decisions based on data. It also helps us identify any potential confounding variables that may influence the relationship between two variables.

5. Can a conditional distribution be used to make predictions?

Yes, a conditional distribution can be used to make predictions. By understanding the relationship between two variables, we can use the conditional distribution to calculate the probability of a certain outcome given the occurrence of another event. This can be useful in various fields, such as finance, healthcare, and marketing, to make informed decisions and predictions.

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