語言分析與資料科學

機率與機率分佈

P(x) = \frac{1}{\sigma\sqrt{2\pi}}e^{-\left[\frac{(x - \mu)^2}{2\sigma^2} \right]}

$\sigma$ refers to the standard deviation.
$\mu$ refers to the mean.
$e$ is a constant, respectively, the base of the natural log system and approximately equals to 2.718.
$\pi$ a constant with an approximate value of $\frac{22}{7}$ or 3.1416.
$x$ refers to the value of the random variable.

rnorm(n=10)
 
[1] -0.48200811  1.02070719  0.09263650  0.07460888  1.75003405  
0.22843413
 [7] -0.11792523  0.39054810 -0.35487301 -1.82808605

P(x) = \frac{\mu^{x}e^{-\mu}}{x!}

Last updated 4 years ago

機率密度函數：隨機變數值 $x$ 和機率 $\mathcal{P}$ 之間的函數關係。

P(x) = \frac{1}{\sigma\sqrt{2\pi}}e^{-\left[\frac{(x - \mu)^2}{2\sigma^2} \right]}

$\sigma$ refers to the standard deviation.

$\mu$ refers to the mean.

$e$ is a constant, respectively, the base of the natural log system and approximately equals to 2.718.

$\pi$ a constant with an approximate value of $\frac{22}{7}$ or 3.1416.

$x$ refers to the value of the random variable.

P(x) = \frac{\mu^{x}e^{-\mu}}{x!}