Difference between r1.2 and the current
@@ -1,9 +1,9 @@
'''Normal Random Variable'''
$S_X=(-\infty,+\infty)$
$f_X(x)=\frac{e^{-\frac{(x-m)^2}{2\sigma^2}}}{\sqrt{2\pi}\sigma}=\frac{e^{-(x-m)^2/2\sigma^2}}{\sqrt{2\pi}\sigma}$
$E[X]=m$
$V[X]=\sigma^2$
AKA '''Gaussian Random Variable''' (가우시안/가우스 확률변수)
AKA '''Gaussian Random Variable''' (가우시안 확률변수, 가우스 확률변수)
$S_X=(-\infty,+\infty)$
$f_X(x)=\frac{e^{-\frac{(x-m)^2}{2\sigma^2}}}{\sqrt{2\pi}\sigma}=\frac{e^{-(x-m)^2/2\sigma^2}}{\sqrt{2\pi}\sigma}$
$-\infty<x<\infty$ and $\sigma>0$
$-\infty \lt x \lt \infty$ and $\sigma \gt 0$
$E[X]=m$
$V[X]=\sigma^2$
Normal Random Variable
AKA Gaussian Random Variable (가우시안 확률변수, 가우스 확률변수)
AKA Gaussian Random Variable (가우시안 확률변수, 가우스 확률변수)
$\displaystyle S_X=(-\infty,+\infty)$
$\displaystyle f_X(x)=\frac{e^{-\frac{(x-m)^2}{2\sigma^2}}}{\sqrt{2\pi}\sigma}=\frac{e^{-(x-m)^2/2\sigma^2}}{\sqrt{2\pi}\sigma}$
$\displaystyle V[X]=\sigma^2$
$\displaystyle f_X(x)=\frac{e^{-\frac{(x-m)^2}{2\sigma^2}}}{\sqrt{2\pi}\sigma}=\frac{e^{-(x-m)^2/2\sigma^2}}{\sqrt{2\pi}\sigma}$
$\displaystyle -\infty \lt x \lt \infty$ and $\displaystyle \sigma \gt 0$
$\displaystyle E[X]=m$$\displaystyle V[X]=\sigma^2$
