Finding k from Moment Generating Function at t=0

In summary, the value of k is found to be 1/625 by using the property that the moment generating function evaluated at t=0 is equal to the integral of the probability density function, which is equal to 1 for a continuous random variable. This approach is more effective than trying to differentiate and expand the moment generating function.
  • #1
little neutrino
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Homework Statement


If M[X(t)] = k (2 + 3e^t)^4 , what is the value of k

Homework Equations


M[X(t)] = integral ( e^tx * f(x) )dx if X is continuous

The Attempt at a Solution


I tried differentiating both sides to find f(x), but since it is a definite integral from negative infinity to infinity this method doesn't work. Is this approach (trying to find f(x)) correct? If so, how should I proceed from here? Even if I expand (2 + 3e^t)^4 the resulting expression will be very convoluted and hard to work backwards with. Thanks!
 
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  • #2
Hint: what properties must a moment generating function have? i.e. there is a particularly helpful property of moment generating functions that would help here.
 
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  • #3
axmls said:
Hint: what properties must a moment generating function have? i.e. there is a particularly helpful property of moment generating functions that would help here.

Hmm, I'm not sure, is it the Taylor series expansion of M(t)? I worked out the first few terms but doesn't seem to help. I'll just get M(t) = k(2+3e^t)^4 = M(0) + M'(0)t + (M''(0)/2!)t^2 +... = 625k + 1500kt + ... (did not expand the rest, because I don't think it's getting me anywhere)
I don't think this is the particularly helpful property you are referring to... Any hints on what the property is? Thanks!
 
  • #4
I'll expand on the hint a little. Note that for a continuous random variable, the moment generating function is, as you have pointed out, $$M(t)=\int _{-\infty} ^{\infty} e^{tX} f(x) \ dx$$
What, then, does this say about the value of any moment generating function at ##t = 0##?
 
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  • #5
axmls said:
I'll expand on the hint a little. Note that for a continuous random variable, the moment generating function is, as you have pointed out, $$M(t)=\int _{-\infty} ^{\infty} e^{tX} f(x) \ dx$$
What, then, does this say about the value of any moment generating function at ##t = 0##?

Ok I got it! M(0) = integral f(x) = 1
k = 1/625
Thanks for your help!
 
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1. What is a moment generating function?

A moment generating function is a mathematical function that provides a way to calculate the moments (mean, variance, skewness, etc.) of a probability distribution. It is defined as the expected value of e^tx, where t is a parameter and x is a random variable.

2. How is a moment generating function different from a probability generating function?

A moment generating function is defined for continuous random variables, while a probability generating function is defined for discrete random variables. Additionally, a moment generating function can be used to calculate any moment of a distribution, while a probability generating function can only be used to calculate the first moment (mean) of a distribution.

3. What are the advantages of using moment generating functions?

Moment generating functions have several advantages. They can be used to calculate any moment of a distribution, making them more versatile than other methods. They also provide a way to easily calculate the moments of a sum of independent random variables, which is useful in many applications. Additionally, they have a unique property that allows for the calculation of the distribution of a sum of random variables, even when the individual distributions are unknown.

4. Can moment generating functions be used for all types of probability distributions?

No, moment generating functions can only be used for distributions that have finite moments. This means that they cannot be used for distributions with heavy tails or infinite variance, such as the Cauchy distribution.

5. How are moment generating functions used in statistical analysis?

Moment generating functions are often used in statistical analysis to calculate the moments of a distribution, such as the mean and variance. They can also be used to prove theorems in probability theory and to derive the properties of different distributions. Additionally, they are used in hypothesis testing and confidence interval calculations.

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