- #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?
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?