Why is the MGF the Laplace transform?

In summary, the conversation discusses the relationship between the Laplace transform and the moment generating function of a probability distribution. While the Laplace transform can capture both exponential and oscillatory components of a function, it requires the use of complex numbers. On the other hand, the moment generating function is limited to real numbers, and therefore cannot capture oscillatory components. However, it is still useful for extracting moments of a distribution. The conversation also touches on the use of complex numbers in the Laplace transform and the concept of inverting the transform.
  • #1
Joan Fernandez
3
0
TL;DR Summary
The MGF of a probability distribution is its Laplace transform. However, LTs have domain and codomains in the complex plane, whereas MGFs are real. Why is this not an issue?
The Laplace transform gives information about the exponential components in a function, as well as oscillatory components. To do so there is a need for the complex plane (complex exponentials).

I get why the MGF of a distribution is very useful (moment extraction and classification of the distribution in terms of its tails (exponential, subexponential, fat tailed, etc). But since the MGF has as domain an interval in the real line in which E[exp{tX}] is defined, and maps to [0, infinity], all the analysis of oscillatory components in the function is left out, and within the realm of the characteristic function. All that the MGF achieves is the "scalar product" with an exponential function. How can therefore be called the Laplace transform?
 
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  • #2
[tex]\frac{d}{dt} e^{tX}=X e^{tX}[/tex]
for any complex variable t. So you don't have to limit t a real number. In the case t is pure imaginary number it is called Fourier transform. So in general complex t, the transformation would be called Laplace-Fourier transform. The final results ##E^{(n)}(t=0)## remain real.
 
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  • #3
anuttarasammyak said:
[tex]\frac{d}{dt} e^{tX}=X e^{tX}[/tex]
for any complex variable t. So you don't have to limit t a real number. In the case t is pure imaginary number it is called Fourier transform. So in general complex t, the transformation would be called Laplace-Fourier transform. The final results ##E^{(n)}(t=0)## remain real.
In terms of recovering moments it makes no difference, but what about capturing any oscillating sinusoidal components?
 
  • #4
Even in usual case of real t, I am afraid you do not find a meaning of ##e^{tX}##. Neither I do not think you have to worry about meaningless in ##e^{tX}## for pure imaginary or complex t.
 
  • #5
Joan Fernandez said:
Summary:: The MGF of a probability distribution is its Laplace transform. However, LTs have domain and codomains in the complex plane, whereas MGFs are real. Why is this not an issue?

The Laplace transform gives information about the exponential components in a function, as well as oscillatory components. To do so there is a need for the complex plane (complex exponentials).

I get why the MGF of a distribution is very useful (moment extraction and classification of the distribution in terms of its tails (exponential, subexponential, fat tailed, etc). But since the MGF has as domain an interval in the real line in which E[exp{tX}] is defined, and maps to [0, infinity], all the analysis of oscillatory components in the function is left out, and within the realm of the characteristic function. All that the MGF achieves is the "scalar product" with an exponential function. How can therefore be called the Laplace transform?

If [itex]f[/itex] is real-valued, then [tex]
\int_0^\infty f(x)e^{-tx}\,dx[/tex] is real-valued for real [itex]t[/itex]. What set [itex]t[/itex] is in depends on your application. If you want to invert the transform, then [itex]t[/itex] must be complex. On the other hand, if you want to extract [tex]\int_0^\infty x^n f(x)\,dx = \left. (-1)^n \frac{d^n}{dt^n}\left(\int_0^\infty f(x)e^{-tx}\,dx\right)\right|_{t=0}[/tex] then [itex]t[/itex] only needs to be in an interval which includes 0.
 
  • #6
##\int\limits_0^\infty e^{-tx}f(x)dx=\sum\limits_{n=0}^\infty \frac{(-t)^n}{n!}\int\limits_0^\infty x^nf(x)dx##. Which can give moments for non-negative random variables. The Fourier transform removes the non-negative restriction.
 
  • #7
pasmith said:
If [itex]f[/itex] is real-valued, then [tex]
\int_0^\infty f(x)e^{-tx}\,dx[/tex] is real-valued for real [itex]t[/itex]. What set [itex]t[/itex] is in depends on your application. If you want to invert the transform, then [itex]t[/itex] must be complex. On the other hand, if you want to extract [tex]\int_0^\infty x^n f(x)\,dx = \left. (-1)^n \frac{d^n}{dt^n}\left(\int_0^\infty f(x)e^{-tx}\,dx\right)\right|_{t=0}[/tex] then [itex]t[/itex] only needs to be in an interval which includes 0.
Right, I follow... So my question could really be re-phrased as: when t is complex we have a bona fides LT, but what is it that we are doing when in your first integral t is real? Are we just "dotting" the function with an exponential to see how it decays?
 
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FAQ: Why is the MGF the Laplace transform?

Why is the moment generating function (MGF) important?

The MGF is important because it is a mathematical tool used to characterize the probability distribution of a random variable. It allows us to calculate moments, such as mean and variance, which are important in statistical analysis and decision making.

What is the relationship between the MGF and the Laplace transform?

The MGF and the Laplace transform are mathematically related. The MGF is the Laplace transform evaluated at a specific value, while the Laplace transform is the MGF evaluated over a range of values. Essentially, the MGF is a special case of the Laplace transform.

How does the MGF help in finding the distribution of a random variable?

The MGF is used to find the distribution of a random variable by providing a way to calculate the moments of the variable. By taking derivatives of the MGF, we can determine the moments, which in turn can be used to find the probability distribution of the random variable.

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 MGF exists for that variable. It is commonly used for continuous and discrete random variables, as well as for multivariate distributions.

What are the limitations of using the MGF for finding the distribution of a random variable?

The MGF may not exist for all random variables, and even when it does exist, it may not be easy to find or manipulate. In addition, the MGF may not provide an accurate representation of the distribution for certain types of random variables, such as those with heavy tails or outliers.

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