Linear Transformation of Gaussian Random Variable
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Theorem
Let $\mu$, $\alpha$ and $\beta$ be real numbers.
Let $\sigma$ be a positive real number.
Let $X \sim \Gaussian \mu {\sigma^2}$ where $\Gaussian \mu {\sigma^2}$ is the Gaussian distribution with parameters $\mu$ and $\sigma^2$.
Then:
- $\alpha X + \beta \sim \Gaussian {\alpha \mu + \beta} {\alpha^2 \sigma^2}$
Proof
Let $Z = \alpha X + \beta$.
Let $M_Z$ be the moment generating function of $Z$.
We aim to show that:
- $Z \sim \Gaussian {\alpha \mu + \beta} {\alpha^2 \sigma^2}$
By Moment Generating Function of Gaussian Distribution and Moment Generating Function is Unique, it is sufficient to show that:
- $\map {M_Z} t = \map \exp {\paren {\alpha \mu + \beta} t + \dfrac 1 2 \alpha^2 \sigma^2 t^2}$
We also have, by Moment Generating Function of Gaussian Distribution, that the moment generating function of $X$, $M_X$, is given by:
- $\map {M_X} t = \map \exp {\mu t + \dfrac 1 2 \sigma^2 t^2}$
We have:
\(\ds \map {M_Z} t\) | \(=\) | \(\ds e^{\beta t} \map {M_X} {\alpha t}\) | Moment Generating Function of Linear Combination of Independent Random Variables | |||||||||||
\(\ds \) | \(=\) | \(\ds e^{\beta t} \map \exp {\alpha \mu t + \frac 1 2 \sigma^2 \paren {\alpha t}^2}\) | ||||||||||||
\(\ds \) | \(=\) | \(\ds \map \exp {\paren {\alpha \mu + \beta} t + \frac 1 2 \sigma^2 \alpha^2 t^2}\) | Exponential of Sum |
$\blacksquare$