Moment generating function to calculate the mean and variance

In summary, the conversation is about a set of questions that involve a distribution with a parameter λ. The question asks for the mean and variance, and the answers will contain λ since it is part of the input. The first question clarifies that λ is between 0 and 1, while the second question states that λ is any positive value. The method for finding the moment generating function involves multiplying by e^tx and using the formula for a geometric series. The purpose of specifying the range of λ is to avoid negative or invalid probabilities in the calculations.
  • #1
snesnerd
26
0
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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  • #2
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
 
  • #3
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 probabilty 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].
 
  • #4
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?
 
  • #5
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
 
  • #6
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.
 

1. What is a moment generating function (MGF)?

A moment generating function is a mathematical tool used to calculate the moments of a random variable, such as the mean and variance. It is defined as the expectation of e^(tx), where t is a constant and x is the random variable.

2. How does the MGF help in calculating the mean and variance?

The MGF allows us to find the moments of a random variable by taking derivatives of the function and evaluating them at t=0. The first derivative gives the mean and the second derivative gives the variance.

3. Can the MGF be used for any type of random variable?

Yes, the MGF can be used for any type of random variable as long as the expectation of e^(tx) is defined for all values of t. It is particularly useful for continuous random variables, but can also be applied to discrete random variables.

4. Are there any limitations to using the MGF for calculating moments?

One limitation is that the MGF may not exist for some types of random variables, such as those with heavy tails. In these cases, alternative methods may need to be used to calculate moments. Additionally, the MGF may become difficult to manipulate for complex random variables with multiple parameters.

5. How can the MGF be used to find the distribution of a random variable?

The MGF uniquely determines the distribution of a random variable, meaning that if two random variables have the same MGF, they have the same distribution. This can be useful in finding the distribution of a random variable by comparing its MGF to known MGFs of common distributions.

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