Moment generating function and expectation

In summary, we discussed the computation of the moment generating function and expectation of a random variable X with a probability mass function of P(j) = 2^(-j), j=1,2,3,... We found that the MGF for X is e^t/(2-e^t) and using this, we computed the expectation of X to be 2.
  • #1
BookMark440
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Homework Statement


Let X denote a random variable with the following probability mass function:
P(j)= 2^(-j), j=1,2,3,...
(a) Compute the moment generating function of X.
(b) Use your answer to part (a) to compute the expectation of X.

Homework Equations


m.g.f of X is M (t) = E[e^tX]


The Attempt at a Solution



I computed the MGF (X) to be: e^t/(2-e^t). I need a suggestion about the next step. I'm confused about the relationship between the MGF and expectation of X.

Thanks!
 
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  • #3
That link was very helpful. I completed the problem but I am a little uncertain about my strategy of finding the derivative by parts and the answer:

X has p.d.f : p(j) = 2^(-j), j=1,2,3,...

I computed the MGF for X = e^t/(2-e^t)

Then E[X] = d/dt (e^t/(2-e^t)) [evaluated for t= 0] =

numerator = (2-e^t)d/dt(e^t) - (e^t)d/dt(2-e^t)
denominator = (2-e^t)^2

Evaluating for t=0, the final answer is:
E[X] = 2

Is this the correct strategy and answer?

Thanks!
 
  • #4
Hi BookMark440! :smile:

(try using the X2 tag just above the Reply box :wink:)
BookMark440 said:
Then E[X] = d/dt (e^t/(2-e^t)) [evaluated for t= 0] =

numerator = (2-e^t)d/dt(e^t) - (e^t)d/dt(2-e^t)
denominator = (2-e^t)^2

Evaluating for t=0, the final answer is:
E[X] = 2

Is this the correct strategy and answer?!

Looks good! :biggrin:
 

Related to Moment generating function and expectation

1. What is a moment generating function?

A moment generating function (MGF) is a mathematical function that uniquely characterizes a probability distribution. It is used to find the moments (mean, variance, etc.) of a random variable and to derive the probability distribution of that variable.

2. Why is the moment generating function important?

The moment generating function is important because it allows us to find the moments of a random variable, which are useful for understanding and describing the characteristics of a probability distribution. It also allows us to determine the distribution of a random variable, making it a powerful tool in statistical analysis and hypothesis testing.

3. How do you calculate the moment generating function?

The moment generating function is calculated by taking the expected value of etX, where X is the random variable and t is a real number. This can be done using the formula E[etX] = ∫-∞ etx f(x) dx, where f(x) is the probability density function of X.

4. What is the relationship between the moment generating function and the expectation of a random variable?

The moment generating function and the expectation of a random variable are closely related. The moment generating function evaluated at t=0 is equal to the expectation of the random variable, i.e. MGF(t=0) = E[X]. Additionally, the derivatives of the moment generating function at t=0 are equal to the moments of the random variable, i.e. MGF(k)(t=0) = E[Xk].

5. Can the moment generating function be used for any type of probability distribution?

Yes, the moment generating function can be used for any type of probability distribution, as long as the expected value of etX exists. This includes common distributions such as the normal, binomial, and Poisson distributions.

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