Integral of Gaussian Distribution
    
    Normalisation
    Prove that, 
    $\int^\infty_{-\infty}e^{-\alpha x^2}dx = \sqrt{\frac{\pi}{\alpha }}$
    
        \begin{align*}
        &\int^\infty_{-\infty}e^{-\alpha x^2}dx\int^\infty_{-\infty}e^{-\alpha y^2}dy =
        \int^\infty_{-\infty}\int^\infty_{-\infty}e^{-\alpha(x^2+y^2)}dxdy = \int^{2\pi}_0d\theta\int^\infty_0re^{-\alpha r^2}dr \\
        &= 2\pi\int^\infty_0\frac{-1}{2\alpha }e^{-\alpha r^2}d(-\alpha r^2)=\frac{-\pi}{\alpha }\int^\infty_0 d(e^{-\alpha r^2}) = \frac{-\pi}{\alpha }[e^{-\alpha r^2}]^\infty_0=\frac{\pi}{\alpha }\\
        &\text{Therefore, } \bigg[\int^\infty_{-\infty}e^{-\alpha x^2}dx\bigg]^2=\frac{\pi}{\alpha } \Rightarrow \int^\infty_{-\infty}e^{-\alpha x^2}dx = \sqrt{\frac{\pi}{\alpha }}
        \end{align*}
    
    
    Gaussian Distribution is given by ,
    
        $$\mathcal{N}(x;\mu,\sigma^2) = \frac{1}{\sqrt{2\pi\sigma^2 }}e^{-\frac{(x-\mu)^2 }{2\sigma^2 }}$$
    
    Sum to 1
    Prove that,$\int^\infty_{-\infty}\mathcal{N}(x;\mu,\sigma^2)dx = 1$
    
        \begin{align*}
        &\frac{1}{\sqrt{2\pi\sigma^2} }\int^\infty_{-\infty}e^{-\frac{(x-\mu)^2}{2\sigma^2}}dx = \frac{1}{\sqrt{2\pi\sigma^2 }}\int^\infty_{-\infty }e^{-\alpha y^2 }dy \\ &= \frac{1}{\sqrt{2\pi\sigma^2} }\sqrt{\frac{\pi}{\alpha}} 
        = \sqrt{\frac{2\pi\sigma^2 }{2\pi\sigma^2 }} =1 
        \end{align*}
    
    Mean
    Prove that, $\lt x\gt =\int^\infty_{-\infty }x\mathcal{N}(x;\mu,\sigma^2 )dx = \mu$
    
Variance
    3rd Moment
    4th Moment
    References