1.二項分佈

from scipy.stats import binom, norm, beta, expon
import numpy as np
import matplotlib.pyplot as plt

binom_sim = data = binom.rvs(n=10, p=0.3, size=10000)
print('Mean: %g' % np.mean(binom_sim))
print('SD: %g' % np.std(binom_sim, ddof=1))
plt.hist(binom_sim, bins=10, normed=True)
plt.xlabel(('x'))
plt.ylabel('density'
 
)
plt.show()

2.正態分佈

# 正態分佈
mu = 0
sigma = 1
x = np.arange(-5, 5, 0.1)
y = norm.pdf(x, 0, 1)
plt.plot(x, y)
plt.xlabel('x')
plt.ylabel('density')
plt.show()

3.泊松分佈

x = np.random.poisson(lam=5, size=10000)  pillar = 15
a = plt.hist(x, bins=pillar, normed=True, range=[0, pillar], color='g', alpha=0.5)
plt.plot(a[1
 
][0:pillar], a[0], 'r')
plt.grid()
plt.show()

3.beta分佈

a = 0.5
b = 0.5
x = np.arange(0.01, 1, 0.01)
y = beta.pdf(x, a, b)
plt.plot(x, y)
plt.title('Beta: a=%.1f,b=%.1f' % (a, b))
plt.xlabel('x')
plt.ylabel('density')
plt.show()

5.指數分佈

lam = 0.5
x = np.arange(0, 15, 0.1)
y = expon.pdf(x, lam)
plt.plot(x, y)
plt.title('Exponential: lam=%.2f' 
 
% lam)
plt.xlabel('x')
plt.ylabel('density')
plt.show()