Moment generating function to calculate the mean and variance

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Homework Help Overview

The discussion revolves around the moment generating function (MGF) in the context of a probability distribution involving a parameter λ. Participants are exploring how to derive the mean and variance of the distribution while grappling with the implications of the constraints on λ provided in the questions.

Discussion Character

  • Conceptual clarification, Assumption checking

Approaches and Questions Raised

  • Participants discuss the process of finding the moment generating function and its relation to the geometric series. Questions arise regarding the significance of the constraints on λ, particularly why specific ranges are provided and how they affect the calculations of mean and variance.

Discussion Status

Some participants express initial confusion about the wording of the questions and the role of λ. Clarifications have been provided regarding the necessity of the constraints on λ to ensure valid probabilities. There appears to be a productive exchange of ideas, with participants attempting to reconcile their understanding of the problem.

Contextual Notes

Participants are working under the assumption that λ is a constant that influences the distribution, with specific ranges provided to avoid invalid probability values. The discussion reflects on the implications of these constraints for the calculations involved.

snesnerd
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I attached a pdf. The questions are not really what is stumping me. Its the wording of the question I don't understand. When it says, "Assume that 0 < λ < 1. Note that your answers will be in terms of the constant λ." and "Assume that λ > 0. Note that your answers will be in terms of the constant λ." I don't understand what they want me to really do here when it says that. To show I know how to do the questions before anyone helps me, I will briefly explain the solution to each one.

1a. Multiply by e^tx. Its a geometric series so just use the formula for the geometric series.
1b. To find the mean of X take the derivative of the answer to 1a.
1c. To find the variance of X take the second derivative minus first derivative squared.

2a. Multiply by e^tx then integrate from 0 to infinity.
2b. Same idea as 1b.
2c. Same idea as 1c.

 

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snesnerd said:
I attached a pdf. The questions are not really what is stumping me. Its the wording of the question I don't understand. When it says, "Assume that 0 < λ < 1. Note that your answers will be in terms of the constant λ." and "Assume that λ > 0. Note that your answers will be in terms of the constant λ." I don't understand what they want me to really do here when it says that. To show I know how to do the questions before anyone helps me, I will briefly explain the solution to each one.

1a. Multiply by e^tx. Its a geometric series so just use the formula for the geometric series.
1b. To find the mean of X take the derivative of the answer to 1a.
1c. To find the variance of X take the second derivative minus first derivative squared.

2a. Multiply by e^tx then integrate from 0 to infinity.
2b. Same idea as 1b.
2c. Same idea as 1c.

I do not understand what your difficulty is. You have a distribution; it contains a parameter λ. You do not know the value of λ, but you are told it is between 0 and 1. The question asks you to determine the mean and variance. Of course there will be a λ in the answers, since there is a λ in the input. What is unclear about that?

RGV
 
1a) asks you to find the moment generating function of the distribution [itex](1- \lambda)\lambda^x[/itex] and you are told "1a. Multiply by [itex]e^tx[/itex]." Okay, the moment generating function is, by definition, the expected value of [itex]e^{tx}[/itex] and the expected value of any function is the sum of function values for each x times the probability of that particular x. Here, that would be
[tex]\sum_{x=0}^\infty (1- \lambda)\lambda^xe^{tx}= (1- \lambda)\sum_{n=0}^\infty (\lambda e^t)^x[/tex]
which is, as they say, a geometric series with common multiple [itex]\lambda e^t[/itex]. Use the fact that the sum of a geometric series is given by
[tex]\sum_{x=0}^\infty r^x= \frac{1}{1- r}[/tex].
 
Thanks everyone. I don't know why I was letting it confuse me, but it seems clarified now. One last question though. What purpose did it serve to tell me that in the first question, 0 < λ < 1, and in the second question λ > 0? Couldnt they of just said that λ is just a constant?
 
snesnerd said:
Thanks everyone. I don't know why I was letting it confuse me, but it seems clarified now. One last question though. What purpose did it serve to tell me that in the first question, 0 < λ < 1, and in the second question λ > 0? Couldnt they of just said that λ is just a constant?

No. In question 1 you need lambda between 0 and 1 in order to avoid negative probabilities or probabilities > 1. Question 2 is completely different; there you could have ANY positive value for lambda (but, again, a negative value would give nonsense such as negative probability, etc).

RGV
 
Gotcha. Thanks. Last question. The sum of the geometric series in the first question is

(1 - λ)/(1 - λe^t) right?

Since the first term is (1 - λ) and the common ratio is λe^t.
 

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