Is My Probability Mass Function Correct Given the MGF?

But I see how it could be misleading. In summary, the pmf of X is given by:x f(x)1 0.32 0.43 0.24 05 0.1Some may prefer to include the zero probability for clarity, while others may choose to omit it in order to simplify the table. Ultimately, both forms are acceptable as long as the information is accurately represented.
  • #1
Tomp
27
0
Question
A moment-generating function of X is given by M(t) = 0.3e^t + 0.4e^(2t) + 0.2e^(3t) + 0.1e^(5t)

Find the pmf of X

My attempt
x f(x)
1 0.3
2 0.4
3 0.2
4 0
5 0.1

I am just wondering whether it is correct to say "0" for 4 or is it more correct to say

x f(x)
1 0.3
2 0.4
3 0.2
5 0.1

or is either acceptable
 
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  • #2
I would prefer to see the second form.
 
  • #3
I, on the other hand, prefer the first. I think if I were to look at the second I would wonder whether or not the "5" was a typo and "4" was meant. The first form makes it clear.

(Though my "inner typesetter" would prefer "4 0.0"!)
 
  • #4
Typesetting preferences :)

HallsofIvy said:
I, on the other hand, prefer the first. I think if I were to look at the second I would wonder whether or not the "5" was a typo and "4" was meant. The first form makes it clear.

(Though my "inner typesetter" would prefer "4 0.0"!)

Understandable. My thought process was simple: in a small discrete distribution, if some integer value has probability zero, don't bother including it in a summary table.
 
  • #5
?Both options are acceptable, as long as the probabilities add up to 1. The moment-generating function does not provide information on the probability of the value 4, so either option is valid. However, in order to be consistent with the given moment-generating function, it may be more appropriate to assign a probability of 0 to the value 4.
 

Related to Is My Probability Mass Function Correct Given the MGF?

What is a moment generating function?

A moment generating function (MGF) is a mathematical function that is used to describe the statistical properties of a probability distribution. It is defined as the expected value of the exponential function raised to the power of the random variable.

Why is the moment generating function useful?

The moment generating function allows for the calculation of moments of a probability distribution, such as mean, variance, and higher order moments. It also simplifies the calculation of probabilities and allows for the derivation of other important functions, such as the cumulant generating function.

How is the moment generating function related to the probability generating function?

The probability generating function (PGF) is a special case of the moment generating function, where the random variable takes on only non-negative integer values. The PGF is used to calculate the probabilities of discrete distributions, while the MGF is used for continuous distributions.

What is the relationship between the moment generating function and the characteristic function?

The characteristic function is the Fourier transform of the probability density function, while the moment generating function is the Laplace transform. This means that they are related, but not equivalent. However, for certain distributions, the MGF and characteristic function can be used interchangeably.

What are the limitations of the moment generating function?

The moment generating function may not exist for all distributions, and even if it does exist, it may not be finite for all values of the parameter. Additionally, the MGF may not uniquely identify a probability distribution, as different distributions can have the same MGF.

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