I'm wondering how to get variance of exp. distribution from the raw variance computed using the moment generating function. Here's my line of reasoning:

PDF of Exponential distriution is

$$ p_X(x) = \lambda \cdot e^{-\lambda x} $$

for $x > 0$, and $0$ for $x \leq 0$.

Deriving the MGF:

$$ \begin{aligned} M_X(t) &= \mathbb{E}\left[e^{t X}\right] && \text{definition} \\ &= \int_{- \infty}^{\infty} x \cdot p_X(x) dx&& \text{just definition of expectation} \\ &= \int_{- \infty}^{\infty} e^{t x} \cdot \lambda e^{-\lambda x} dx&& \text{LOTUS} \\ &= \int_{0}^{\infty} e^{t x} \cdot \lambda e^{-\lambda x} dx&& \text{since } x > 0 \\ &= \lambda \int_{0}^{\infty} e^{t x} \cdot e^{-\lambda x} dx&& \text{the constant multiple rule} \\ &= \lambda \int_{0}^{\infty} e^{t x -\lambda x} dx \\ &= \lambda \int_{0}^{\infty} e^{x (t -\lambda )} dx \\ &= \lambda \cdot \frac{1}{\lambda - t} && \text{closed form solution for } t < \lambda \\ &= \frac{\lambda}{\lambda - t} \qquad \boxed{\checkmark} \text{ Wikipedia check} \end{aligned} $$

Getting moments of exponential distributions by derivating MGF

$$ M_X(t) = \frac{\lambda}{\lambda - t} $$

First moment (expectation)

$$ M_X^{(1)}(t) = \frac{\partial}{\partial t} \left( \frac{\lambda}{\lambda - t} \right) = \frac{\lambda}{(\lambda - t)^2} $$

  • And evaluate at $t = 0$:

$$ \frac{\lambda}{(\lambda - t)^2} \bigg\vert_{t=0} = \frac{\lambda}{\lambda^2} = \frac{1}{\lambda} \qquad \boxed{\checkmark} \text{ Wikipedia check} $$

Second moment

$$ M_X^{(2)}(t) = \frac{\partial^2}{\partial^2 t} \left( \frac{\lambda}{\lambda - t} \right) = \frac{2 \lambda}{(\lambda - t)^3} $$

$$ \frac{2 \lambda}{(\lambda - t)^3} \bigg\vert_{t=0} = \frac{2}{\lambda^2} $$

So this is raw variance but not the actual variance $\frac{1}{\lambda^2}$... how to get there?


$M_X^{(2)}(0)$ is not a variance, it is $E(X^2)$. So the variance can be obtained by $$Var(X) = E(X^2) - E(X)^2 = M_X^{(2)}(0) - [M_X^{(1)}(0)]^2 = \frac{1}{\lambda^2}$$

  • $\begingroup$ Thanks! So I guess my confusion was due to terminological complexity of moments (raw, central, normalised), as shown for example in the table here en.wikipedia.org/wiki/Moment_(mathematics) $\endgroup$
    – John Doe
    Oct 29 '20 at 8:17
  • 2
    $\begingroup$ There is another useful function related to mgf, which is called a cumulant generating function(cgf, $C_X(t)$). cgf is defined as $C_X(t) = \log M_X(t)$ and its first derivative and second derivative evaluated at $t=0$ are mean and variance respectively. In multivariate cases, we can easily get covariance from cgf. en.wikipedia.org/wiki/Cumulant $\endgroup$
    – flossy
    Oct 29 '20 at 9:00
  • $\begingroup$ You just showed me another piece of puzzle that I was missing somewhere else :) Thanks! $\endgroup$
    – John Doe
    Oct 29 '20 at 15:15

The second moment gives you


and the variance is defined as


so that you get


which will then give you the desired result.

  • $\begingroup$ Yes, thanks, it's now clear to me that the second moment does not equal variance (as apposed to the first moment which is the expectation). $\endgroup$
    – John Doe
    Oct 29 '20 at 8:44
  • $\begingroup$ Nitpick: the variance is defined as $$E[(X−E[X])^2]$$ and from this one derives the more used formula in the answer. $\endgroup$ Oct 29 '20 at 18:11

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